Added

  • Implementation of unit tests
  • Implementation of the documentation for the level 3
  • Development of output extraction functions for process 1.1
  • Adding an output directory intilisation function and integrating it into the processes

Changed

  • Fix minor bugs and optimisation of the code and regarding the overall process
  • Update of the level 3 process

Added

  • Implementation for the import of annual dataset from outputs of the level 1 and 2 (period length customizable)
  • Implementation of boostrap method as base for all confidence intervals
  • Computation of confidence interval (bootstrap interval) for the nominal catches by species and by fishing mode
  • Implementation of outputs for the he nominal catches by species and by fishing mode
  • Fix minor bugs in all different processes (P1, P2, P3)

Changed

  • Improve modelling configuration (addition of new parameters)
  • Rewrite several figures for the model checking
  • Improve time computing using faster implementation of random forests for the species composition modeling in the level3

Removed

Added

  • Implentation of the model layer in R6class
  • Implementation of all logbooks standardization sub-processes
  • Implementation of all samples standardization sub-processes
  • Implementation of sub-processes for assessing species composition in relation to non-sampled assemblages
  • Strict selection of samples used in the model training according to several criteria on sets and wells (school type, location, date, sample quality).
  • Classification of unknown school type (no data available) by clustering according to major tuna species composition.
  • Development of a statistical model accounting for:
    • Spatial and temporal structure of the fishery (location and date of the catch)
    • Vessel specificity which impacts the species composition of the catch
    • Crew reporting information which enables more flexibility in the model when a marginal composition is caught
    • Several years of fishing in the training to increase the robustness of the estimates
  • Model checking to ensure reliability of the results
  • Computation of a confidence interval (bootstrap method) which give the trust level on catch estimations