---
title: "Harvesting models"
package: matreex
output: 
    github_document:
    rmarkdown::html_vignette:
vignette: >
  %\VignetteIndexEntry{Harvesting models}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
bibliography: references.bib
link-citations: yes
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
options(cli.progress_show_after = 600) 
# 10 minutes before showing a cli progress bar

library(tidyr)
```

This vignette illustrates how to use harvesting scenarii with `{matreex}` package. An harvesting scenario provides the rules and the periodicity to trigger harvesting of trees in each size classes. The harvested trees are saved and exported for each simulation time step.

The basic `{matreex}` functions are shown in [a previous introduction vignette](matreex.html).

# Simulations input

## Define a species

The first step is the IPM integration. This part is common with basic usage of the package, so nothing is very important here.

**Please keep in mind this computation is intensive and may take few minutes !**

```{r make_IPM_Picea}
library(matreex)
library(dplyr)
library(ggplot2)

# Load fitted model for a species
# fit_species # list of all species in dataset
data("fit_Picea_abies")

# Load associated climate
data("climate_species")
climate <- subset(climate_species, N == 2 & sp == "Picea_abies", select = -sp)
# see ?climate_species to understand the filtering of N.
climate

Picea_ipm <- make_IPM(
    species = "Picea_abies", 
    climate = climate, 
    fit = fit_Picea_abies,
    clim_lab = "optimum clim", 
    mesh = c(m = 700, L = 90, U = get_maxdbh(fit_Picea_abies) * 1.1),
    BA = 0:80, # Default values are 0:200, smaller values speed up this vignette.
    verbose = TRUE
)
```

```{r hide warn, echo=FALSE, }
options(W_matreex_edist = FALSE)
```

Harvesting scenario are defined at different levels: at the species level with the species harvesting function and its associated parameters, at the forest level with a set of parameters, and at the simulation level with the target of the harvesting scenario. Depending on the scenario used, not all the parameters are used. The table below summarise the required parameters for each scenario. Each scenario have an example in the following document.

| Harvesting scenario | Species harvesting function | Species parameters | Forest parameters | Simulation parameters |
| -- | -- | -- | -- | -- |
| default | `def_harv()` <br> or custom by user. | $\emptyset$ | `harv_rules["freq"]` | $\emptyset$ |
| Uneven | `Uneven_harv()` | `harv_lim` | `harv_rules` | `targetBA` |
| Even | `Even_harv()` | `rdi_coef` | `harv_rules` | `targetRDI` <br> `targetKg` <br> `final_harv` |

Short example : When using an Uneven scenario, each species needs to use the `Uneven_harv()` function with `harv_lim` parameters. The forest will need to have `harv_rules` parameters. The `sim_deter_forest()` require `targetBA` argument. If `targetRDI` is defined, it will not be used.

# Default scenario

## Presentation

The default scenario is based on @kunstler2021. This mean there is a constant harvest rate triggered each year. This harvest rate is uniform over the size distribution. This is the scenario used by default.

This rate is coded in `def_harv()` function as shown below and the frequency (each year) is given in `harv_rules["freq"]`. Note that the harvesting is applied to all size class except the age-classes used in delay (with `* (ct > 0)`). 


```{r def_Picea}
def_harv
Picea_sp <- species(IPM = Picea_ipm, init_pop = def_initBA(30))
Picea_for <- forest(species = list(Picea = Picea_sp), 
                    harv_rules = c(freq = 1))
```

With this scenario, the simulation can be launched without any additional parameter.

```{r sp1sim}
set.seed(42) # The seed is here to replicate the random population initialisation.
Picea_sim <- sim_deter_forest(
    Picea_for,
    tlim = 200, equil_time = 200, equil_dist = 10, equil_diff = 1,
    harvest = "default", # this is the default value but we write it.
    SurfEch = 0.03,
    verbose = TRUE
)
```

Once the simulation is done, we can extract the basal area and the number of individual at each step of the simulation, but also a new variable $H$. This is the sum of densities of harvested trees of all size classes at each time step. The density of harvested trees is also exported for each size mesh $h_i$.

In this case, $H$ is correlated with $N$ since it's a constant percentage not linked with a size distribution. The first step being the initialization step, it's normal to have no harvest.

```{r sp1plot}
Picea_sim  %>%
    dplyr::filter(var %in% c("BAsp", "N", "H"), ! equil) %>%
    ggplot(aes(x = time, y = value)) +
    facet_wrap(~ var, scales = "free_y") +
    geom_line(linewidth = .2) + geom_point(size = 0.4) 
```

## Modulation

This section will just illustrate variation of the default scenario. First we modify the frequency of the harvest. When the harvest is not triggered, the value returned is 0.

```{r def_freq}
set.seed(42) # The seed is here for initial population random functions.
Picea_sim_f20 <- sim_deter_forest(
    forest(species = list(Picea = Picea_sp), 
                      harv_rules = c(freq = 20)),
    tlim = 50, equil_time = 50, equil_dist = 10, equil_diff = 1,
    harvest = "default", 
    SurfEch = 0.03,
    verbose = TRUE
)
Picea_sim_f20  %>%
    dplyr::filter(var %in% c("BAsp", "N", "H"), ! equil) %>%
    ggplot(aes(x = time, y = value)) +
    facet_wrap(~ var, scales = "free_y") +
    geom_line(linewidth = .2) + geom_point(size = 0.4) 
```

A more advanced modification of the harvest function is ilustratted below. Obviously, this type of modification have not been thoroughly tested and thus is more prone to error so don't hesitate to contact `{matreex}` maintainer in case of trouble. For example, we add a function where we multiply a constant rate with the mesh, meaning that the larger the tree get, the higher the harvesting rate of its size class.

```{r edit_def_harv}
Picea_harv <- Picea_sp
Picea_harv$harvest_fun <- function(x, species, ...){

    dots <- list(...)
    ct <- dots$ct

    rate <- 6e-4 * (ct > 0) * species$IPM$mesh 
    return(x * rate)
}

set.seed(42) # The seed is here for initial population random functions.
Picea_sim_f20 <- sim_deter_forest(
    forest(species = list(Picea = Picea_harv), 
                      harv_rules = c(freq = 20)),
    tlim = 250, equil_time = 250, equil_dist = 10, equil_diff = 1,
    harvest = "default",
    SurfEch = 0.03,
    verbose = TRUE
)
Picea_sim_f20  %>%
    dplyr::filter(var %in% c("BAsp", "N", "H"), ! equil) %>%
    ggplot(aes(x = time, y = value)) +
    facet_wrap(~ var, scales = "free_y") +
    geom_line(linewidth = .2) + geom_point(size = 0.4) 
```

# Uneven scenario

## Theory

Uneven-aged harvest scenario consist in harvesting trees in all size classes with the objective to reach a stable size structure with continuous replacing  of large mature trees. This scenario depends on the basal area of the stand and the size distribution of the tree. This should lead to stands with uneven size distribution.

### Monospecific case

#### Harvest proportion

We note $P_{cut}$ the global harvest proportion which will determine the amount of basal area to be harvested.

$$
P_{cut} = \left\lbrace
 \begin{array}{ll}
    0 & if (BA_{stand} - BA_{target}) < \Delta BA_{min}\\
    min(\frac{BA_{stand}-BA_{target}}{BA_{stand}}, P_{max}) & if (BA_{stand} - BA_{target}) \geq \Delta BA_{min}
 \end{array}
 \right\rbrace
$$

For example, some numerical values of the parameters can be $\Delta BA_{min} = 3 m^2ha^{-1}$, $BA_{target} = 20, 25$ or $30 m^2ha^{-1}$ (depending on species) and $P_{max} = 0.25$.

Note that stand basal area $BA_{stand}$ is computed only considering trees with a dbh above $d_{th}$ (see below).

#### Harvest curve

Each tree harvest probability only depends on its diameter ($d$).
There is a minimum diameter for harvest ($d_{th}$), harvest probability then increases with diameter until $d_{ha}$ after which harvest probability is constant.

We therefore considered the harvesting function (which associates a $dbh$ to an harvesting probability, building on the approach used in previous model such as @guillemot2014). 

$$
h(d) = \left\lbrace
 \begin{array}{ll}
    0 & \text{if } d < d_{th} \\
    h_{max} (\frac{d - d_{th}}{d_{ha} - d_{th}})^{k} & \text{if } d_{th}\leq d < d_{ha} \\
    h_{max} & \text{if } d \geq d_{ha} 
 \end{array}
 \right\rbrace
$$

The maximum harvesting for large tree $h_{max}$ can be tuned so that the probability for a large tree to be harvested approaches  1 (for instance $p = 0.999$) after several harvesting operations:
$h_{max} =  1 - \sqrt[n]{1-p}$ with $n$ the number of harvesting operations. Parameter $k$ defines how quickly the harvesting rate increase from $d_{th}$  to $d_{ha}$.

```{r setplot_val, echo = FALSE}
library(latex2exp)
dth <- 17.5
dha <- 57.5
hmax <- 0.8
k <- 2
```

```{r, harvest_curve_plot, echo = FALSE, warning = FALSE, fig.cap = sprintf("Harvest curve example, $d_{th} = %.1fcm$, $d_{ha}=%.1fcm$, $h_{max}=%.1f$, $k=%.0f$.", dth, dha, hmax, k)}
d <- seq(0, 100, length.out=100)
h <- function(d){
   harvecurve <- hmax * ((d - dth)/(dha-dth))^k
   harvecurve[d<dth] <- 0
   harvecurve[d>dha] <- hmax
   return(harvecurve)
}
df <- data.frame(dbh=d, h=h(d))
ggplot(df, aes(x=dbh, y=h)) + geom_line(col='red') + theme_bw() +
	geom_label(x=dth, y=0.05, label=TeX("$d_{th}$")) +
	geom_label(x=dha-5, y=0.05, label=TeX("$d_{ha}$")) +
	geom_label(x=dha-5, y=hmax, label=TeX("$h_{max}$")) +
	geom_vline(aes(xintercept=dha), linetype=2)
```

#### Harvesting algorithm

The algorithm only target tree contributing to $BA_{stand}$, that is the trees above $d_{th}$. The algorithm start to harvest from largest trees to the smallest. The basal area harvested is function of $P_{cut}$ and the density of trees in function of diameter $\phi(x)$.

The basal area harvested is given by

$$ 
BA_{harv} = BA_{th} + BA_{ha}
$$
with $BA_{ha}$ the basal area harvested for trees greater than $d_{ha}$, and $BA_{th}$ the basal area harvested for trees between $d_{th}$ and $d_{ha}$. The algorithm start to harvest from largest trees to the smallest.



The maximum of $BA_{ha}$ is $BA_{ha}^{max}$, and is given by: 

$$ 
BA_{ha} =  h_{max} \pi/4\int_{d_{ha}}^{d_{max}}x^2 \phi(x)dx
$$
where $\phi$ is the density of trees.

$\bullet$ If $BA_{ha}^{max} >= P_{cut} \times BA_{stand}$, there is enough large trees (diameter above $d_{ha}$) so that the harvest will only concern large trees and $BA_{th} = 0$. Here again the trees are harvested from the largest to the smallest.

We then find $d_t$ (with $d_{ha} < d_t < d_{max}$) such that:
$$  BA_{ha} = h_{max} \pi/4 \int_{d_{t}}^{d_{max}}x^2 \phi(x)dx = P_{cut} \times BA_{stand}$$
Thus, the `{matreex}` package will cut the larger trees until $P_{cut} \times BA_{ha} - targetBA <= 0$.


$\bullet$ If $BA_{ha}^{max} < P_{cut} \times BA_{stand}$, we first harvest $BA_{ha}^{max}$ and then compute $k$ such as: $BA_{th} = P_{cut} \times BA_{stand} - BA_{ha}$. 

With 

$$
 BA_{th} =  \pi/4 \int_{d_{th}}^{d_{ha}}h(x)x^2 \phi(x)dx
$$

### Multispecific case

#### Abundance-based preference

As in the monospecific case, we define the global harvest rate $P_{cut} = \frac{BA_{harv}}{BA_{stand}}$.

Here, $BA_{stand}$ is divided between $S$ species: 
$BA_{stand} = \sum_{s=1}^{S} BA_{stand, s}$ and
$BA_{harv} = \sum_{s=1}^{S} BA_{harv, s}$

We note the relative abundance of species $s$, $p_s = \frac{BA_{stand, s}}{BA_{stand}}$, and the harvest rate of species $s$,  
$P_{cut, s} = \frac{BA_{harv, s}}{BA_{stand, s}} = f(p_s) * P_{cut}$

$P_{cut, i} = \frac{BA_{stand, i} - BA_{harv, i}}{BA_{stand, i}} = f(p_i) * P_{cut}'$
By definition,

$$
\begin{array}{ll}
BA_{harv} & = \sum_{s=1}^{S} BA_{harv, s} = \sum_{s=1}^{S} BA_{stand, s} * P_{cut, s} \\
          & = BA_{stand} \sum_{s=1}^{S} p_s * f(p_s) *P_{cut} \\
	  & = BA_{stand} * P_{cut} \\
\end{array}
$$

So that we have the constraint on $f$: 
$\sum_{s=1}^{S} p_s f(p_s) = 1$

The case $f(p_s) = 1$ works, which leads to $P_{cut,s} = P_{cut}$. In that case the harvest rate is the same for every species $s$.

More broadly, we can use the function $$f(p_s) = \frac{p_s^{\alpha - 1}}{\sum_{s=1}^{S} p_s ^{\alpha}}$$, $\forall \alpha > 0$

For $\alpha = 2$, we for example have 

$$f(p_s) =\frac{p_s}{\sum_{s=1}^{S} p_s ^2} $$
The shape of the function $f$ depends on $\alpha$. If $\alpha = 1$, all species are harvested with the same rate irrespective of their relative abundance. For $\alpha < 1$, the abundant species will be less harvested. Finally, for $\alpha > 1$ the most abundant species will be harvested more, in a greater proportion than 1.


#### Favoured species

In some case, we may want to favour some species compared to others.
We note $q$ the set of $Q$ species we want to favour, and $p_q=\sum_{s=1}^{Q} p_s$
We first compute the harvest rate $P_{cut,q}$ and $P_{cut,1-q}$ for respectively the favoured/other species.

If $p_q \geq 0.5$, we take $P_{cut,q} = P_{cut,1-q} = P_{cut}$ (the species to be favoured are already dominant).


If $p_q < 0.5$, we compute $P_{cut,q}=f(p_q) P_{cut}$ and $P_{cut,1-q}=f(1-p_q) P_{cut}$ with $\alpha > 1$.
By definition, we will get $P_{cut,q} \leq  P_{cut}$.

We then apply for each species $s$ the harvest rate $P_{cut,s} = P_{cut,q}$ or $P_{cut,s}=P_{cut,1-q}$ depending on which group it belongs to.


## Examples

All the parameters described above are input either in the `species()`, `forest()` or `sim_deter_forest()` functions. Additional parameter `dBAmin` is the difference between `BA` and `targetBA` under which an harvesting will not be triggered. 

When ploting the result, keep in mind the difference between `BAsp` and `BAstand`. Only the second one will match with `targetBA`, since during uneven harvesting, trees below `dth` are excluded from computations.

```{r Uneven_Picea}
Picea_Uneven <- species(IPM = Picea_ipm, init_pop = def_initBA(30), 
                        harvest_fun = Uneven_harv,
                        harv_lim = c(dth = 175, dha = 575, hmax = 1))
Picea_for_Uneven <- forest(species = list(Picea = Picea_Uneven), 
                      harv_rules = c(Pmax = 0.25, dBAmin = 3, 
                                     freq = 5, alpha = 1))
```



```{r Uneven_sim}
set.seed(42) # The seed is here for initial population random functions.
Picea_sim_f20 <- sim_deter_forest(
    Picea_for_Uneven,
    tlim = 260, equil_time = 260, equil_dist = 10, equil_diff = 1,
    harvest = "Uneven", targetBA = 20, # We change the harvest and set targetBA.
    SurfEch = 0.03,
    verbose = TRUE
)
Picea_sim_f20  %>%
    dplyr::filter(var %in% c("BAsp", "BAstand", "N"), ! equil) %>%
    ggplot(aes(x = time, y = value)) +
    facet_wrap(~ var, scales = "free_y") +
    geom_line(linewidth = .2) + geom_point(size = 0.4) 
```

We notice that the basal area obtained by the simulation is higher than the targeted one. This can be explained by the fact that the cutting calculation is done on $BA_{stand}$, which does not take into account individuals smaller than $d_{th}$.

# Even scenario

<!-- $$ -->
<!-- dg = \sqrt{\frac{\sum_{i = 0}^n d_i^2 x_i}{ \sum_{i = 0}^n x_i }} \\ -->
<!-- dg_{cut} = \sqrt{\frac{\sum_{i = 0}^n d_i^2 x_i Pc_i }{ \sum_{i = 0}^n x_i Pc_i }} -->
<!-- $$ -->

<!-- mesh in cm. -->
<!-- $$ -->
<!-- RDI = \frac{ \sum_{i = 0}^n x_i }{ e^{ RDI_{int} + RDI_{slope} \times \frac{ \log( \frac{ \sum_{i = 0}^n mesh_i^2  x_i }{ \sum_{i = 0}^n x_i} ) }{2}   } } -->
<!-- $$ -->

The objective of even harvesting is to apply harvesting typical of even-aged harvesting, where all trees started growing roughly at the same age, representing a single cohort. The tree are are harvested with thinning during the forest developpment till the final harvest. Thinning harvest are based on the distance to a self-thinning boundary. This self-thinning boundary is given for each species following :

$$N_{max} = e^{intercept+slope \cdot log(Dg)}$$
The species parameters are given in @Aussenac2021 and inside the package with `rdi_coef` table. $Dg$ is the mean quadratic diameter of trees.

```{r rdi_coef}
data(rdi_coef)
rdi_coef <- drop(as.matrix(
    rdi_coef[rdi_coef$species == "Picea_abies",c("intercept", "slope")]
))
rdi_coef
```

From this, we can compute the density index $RDI = N / N_{max}$ with $N$ the number of stems (@Reineke1933).

The algorithm present in `Even_harv` is triggered if $RDI > targetRDI$ and then harvest trees depending on their diameters.

The degree of preference for small trees in the thinning harvest will depend on the parameter $Kg$ (provided by the user), which is the ratio between the mean quadratic diameter of harvested trees $Dg_d$ and the mean quadratic diameter $Dg$ :

$$Kg = Dg^2_d / Dg^2$$

The algorithm present in `Even_harv` optimises two parameters $h_{max}$ and $k$ of the harvesting function to reach $targetKg$ and $targetRDI$.

$$h(d) = hmax * d^{-k}$$

```{r, even_hcurve_plot, echo = FALSE, warning = FALSE, fig.cap = "Harvest curve example with various combination of hmax and k"}
h_fun <- function(hmax, k,n = 100){
    d <- seq(0, n, length.out=n)
   harvecurve <- hmax * (d)^(-k)
   return(harvecurve)
}

n = 100

expand.grid(hmax = c(.5, .8), k = c(0.1, .5)) %>%
    rowwise() %>%
    mutate(pcut = list(h_fun(hmax, k, n)), dbh = list(1:n)) %>%
    tidyr::unnest(c(pcut, dbh)) %>%
    mutate(hmax = as.factor(hmax), k = as.factor(k)) %>%
    ggplot( aes(x=dbh, y=pcut, col = hmax,  linetype = k)) + 
    geom_line() +
    theme_bw()
```


The last parameter for an even harvested simulation is the final cut time. The stand will be harvested with the previous rules at a given frequency but at a certain point, the manager will harvest all trees and plant new ones. This time is dependant on the species growth and differents targets. The value is named `final_harv` in `sim_deter_forest()` function.

**Because the rdi coefficient depends on a species, it's not possible to use multiple species forest with {matreex} package. Please contact authors if you need to use mutlispecific even harvesting.**

<!-- Mortality is triggered when stand density exceeds the self-thinning boundary, i.e. when $DI$  is greater than or equal to 1. In that case, $DI$ is reduced to 1 using the $Kg$ parameter, with -->

<!-- To reduce $DI$ to 1, the number of trees in the stand is reduced while the mean quadratic diameter is increased, which amounts to preferentially killing small trees. In mixed stands, the number of trees killed for each species is defined according to their proportion in the stand before mortality process. -->

## Multispecific case

Because the species parameters are given in @Aussenac2021 for a single species stand, computing $N_{max}$ and $Dg$ for multispecific stands is not straightforward. 

The most simple approach to deal with is to sum the partial RDi of each species (which ignore interactive effect in the $N_{max}$ between species) as done in @delRo2015 and @Aussenac2021.

The stand RDI is the sum of the RDI of each species : $RDI_{stand} = \sum_{s=1}^{S} N_s / N_{max,s}$

The mean quadratic diameter is computed with all species at once :

$$Dg^2 = \frac{\sum_{s=1}^{S} \sum_{n=1}^{m} n_{s,n} \cdot dbh_{s,n}^2}{\sum_{s=1}^{S} N_{s}}$$

## Examples

The rdi parameters are input in the `species()` function. Calling `forest()` function is not different, only the frequency of harvest will be used. The different targets `targetKg`, `targetRDI` and `final_harv` values are set when launching a simulation.

```{r Even_Picea}
Picea_Even <- species(
    IPM = Picea_ipm, init_pop = def_init_even,
    harvest_fun = Even_harv, rdi_coef = rdi_coef,
    harv_lim = c(dth = 175, dha = 575, hmax = 1)
)

Picea_for_Even <- forest(species = list(Picea = Picea_Even),
                      harv_rules = c(freq = 10))
```


```{r Even_sim}
set.seed(42) # The seed is here for initial population random functions.
Picea_sim_f20 <- sim_deter_forest(
    Picea_for_Even,
    tlim = 100, equil_time = 100, equil_dist = 10, equil_diff = 1,
    harvest = "Even", targetRDI = 0.55, targetKg = 0.6,
    final_harv = 150, SurfEch = 0.03, verbose = TRUE
)
Picea_sim_f20  %>%
    dplyr::filter(var %in% c("BAsp", "N", "H"), ! equil) %>%
    ggplot(aes(x = time, y = value)) +
    facet_wrap(~ var, scales = "free_y") +
    geom_line(linewidth = .2) + geom_point(size = 0.4)
```

This chunk is used to add another species *Abies alba* and is no different than the creation of *Picea abies* species.

```{r make_IPM_Abies}
data("fit_Abies_alba")
Abies_ipm <- make_IPM(
    species = "Abies_alba", 
    climate = climate, # this variable is defined at the top of the doc.
    fit = fit_Abies_alba,
    clim_lab = "optimum clim",
    mesh = c(m = 700, L = 90, U = get_maxdbh(fit_Abies_alba) * 1.1),
    BA = 0:80, # Default values are 0:200, smaller values speed up this vignette.
    verbose = TRUE
)

data(rdi_coef)
rdi_Ab <- drop(as.matrix(
    rdi_coef[rdi_coef$species == "Abies_alba",c("intercept", "slope")]
))

Abies_sp <- species(
    IPM = Abies_ipm, init_pop = def_init_even,
    harvest_fun = Even_harv, rdi_coef = rdi_Ab,
    harv_lim = c(dth = 175, dha = 575, hmax = 1)
)
```

```{r Multisp_sim}
PiAb_for_Even <- forest(
    species = list(Picea = Picea_Even, Abies = Abies_sp),
    harv_rules = c(freq = 10))
set.seed(42) # The seed is here for initial population random functions.
PiAb_sim_f20 <- sim_deter_forest(
    PiAb_for_Even,
    tlim = 200, equil_time = 200, equil_dist = 10, equil_diff = 1,
    harvest = "Even", targetRDI = 0.9, targetKg = 0.9,
    final_harv = 150, SurfEch = 0.03,
    verbose = TRUE
)
```

An additional function of the package is available for computing $Kg$ and $RDI$ after the simulations along time.

```{r Comp_rdi, fig.cap="Summary variables for mutlispecific simulation with even harvesting function. Harvest frequence of 10 years and a final harvest at 150y. Dashed lines represent RDI and Kg targets. Both values where set to 0.9."}
rdi_kg <- sim_rdikg(sim = PiAb_sim_f20, rdi_c = NULL)

PiAb_sim_f20 %>%
    filter(var %in% c("N", "BAsp", "H")) %>%
    select(species, time, var, value) %>%
    bind_rows(rdi_kg) %>%
    filter(var != "Dgcut2") %>%
    ggplot(aes(x = time, y = value, color = species)) +
    geom_line() +
    facet_wrap(~ var, scales = "free_y") +
    geom_hline(data = data.frame(yint=0.9,var=c("Kg", "rdi")), 
               aes(yintercept = yint), linetype = "dashed")
```

# References

<div id="refs"></div>
