Optimization of Sampling of Small Pelagic Fishes in the Exclusive Economic Zone of Senegal under the Climate Impact
Bibliographic record
Abstract
Senegal is in a very favourable geographical position for sea fishing. Its coast has an upwelling favouring a good development of phytoplankton very appreciated by the various fish families that populate its Exclusive Economic Zone (EEZ). The little pelagic fish make up the majority of landings. The dynamics of this family of resources is very complex while its perfect mastery is essential for a fishing well controlled maritime. The mathematical models that exist in the literature have not address the different issues related to maritime fisheries and climate change in the Senegalese fishing areas. The linear programming model in integer numbers has been developed after calculation of equilibrium biomass, catches at equilibrium catchability by the application of Schaefer and Freon models in the Senegalese Economic Exclusive Zone. Two proposals have been developed to better explain the tools used in the writing of the mathematical model. The simulation results were led to the design of a linear integer Program (PLNE). The objective is to maximize the biomass of this family of fish resources on the Petite C\^ote, Grande C\^ote and Cape Verde depending on samples and climate change. In the application of the model, real data made it possible to test the Linear program in integer numbers obtained. This optimization study allowed us to find an effective way to maximize recruitment within this resource family. This consists in setting up several less expensive marine refuges to build in the fishing zones targeted by the study. The simulation computer program of the model is presented in the appendix.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".