% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/planning.R
\name{assess_gear_feasibility}
\alias{assess_gear_feasibility}
\title{Assess gear deployment feasibility at survey locations}
\usage{
assess_gear_feasibility(
  result,
  gear_types = names(.gear_specs),
  depth_col = "depth",
  current_col = "current_velocity",
  substrate_col = "substrate_hardness_class",
  wave_col = NULL,
  verbose = TRUE
)
}
\arguments{
\item{result}{Dataframe from \code{\link[=predict_oyster]{predict_oyster()}} with environmental columns.}

\item{gear_types}{Character vector of gear types to assess. Options:
\code{"longline"}, \code{"bottom_culture"}, \code{"intertidal_rack"}, \code{"raft"},
\code{"restoration_reef"}. Default: all five.}

\item{depth_col}{Character. Column name for depth below CD in metres
(default \code{"depth"}; positive = subtidal).}

\item{current_col}{Character. Column name for current velocity in m/s
(default \code{"current_velocity"}).}

\item{substrate_col}{Character. Column for substrate class
(default \code{"substrate_hardness_class"}). Can be the output of
\code{\link[=classify_substrate_from_backscatter]{classify_substrate_from_backscatter()}}.}

\item{wave_col}{Character or NULL. Column for significant wave height Hs
in metres (default NULL -- wave check skipped if absent).}

\item{verbose}{Logical. Print feasibility summary (default TRUE).}
}
\value{
Input dataframe with added columns per gear type:
\verb{gear_<type>_feasible} (logical),
\verb{gear_<type>_score} [0-1],
plus \code{best_gear} (most feasible option) and \code{n_gear_options} (count).
}
\description{
Evaluates which aquaculture and restoration gear types are physically viable
at each survey location based on depth, current velocity, substrate, and
(optionally) wave height. Returns a feasibility score and binary flag for
each gear type.

This function is part of the \strong{planning module} aimed at commercial
operators. For pure restoration work, the \code{restoration_reef} gear type is
always included and is the most relevant output.
}
\examples{
\donttest{
result <- predict_oyster(survey, "ostrea_edulis")
result <- assess_gear_feasibility(result)

# Sites where longline is feasible AND suitability is High
longline_sites <- subset(result,
  gear_longline_feasible & suitability_class == "High")

# Restoration reef sites (no farming licence needed)
reef_sites <- subset(result, gear_restoration_reef_feasible)
}
}
