Stock assessment of the American lobster stock (Homarus americanus) in the French archipelago of Saint Pierre & Miquelon
Bibliographic record
Abstract
American lobster (Homarus americanus) is one of the most targeted species on the Northeastern American coast. In Canada, 90,000 tons of lobster are landed each year. On the French archipelago of Saint-Pierre & Miquelon, the lobster fishery has developped in response to the declined the snow crab landings. However, no studies on the lobster population have yet been conducted. There is an overall TAC of 30 tons, not however based on scientific advice, since no scientific study has yet been conducted on this stock. First, environmental survey probes have been placed in the archipelago fishermen's traps, which will provide a better understanding of the environmental characteristics of the lobster fishing areas. Then, concentrators recording the GPS position were also installed on the boats of the archipelago. These GPS data provided a map of the different fishing zones, as well as to quantify the fishing pressure applied to them. Finally, field surveys and data collection from different sources were carried out during this internship. These field data aimed to complete two objectives. First, the biological characteristics of the lobster population have been studied. Then, these size-structured data were used to feed a stock assessment model using the pseudo-cohort method, which was adjusted for abundance and effort over the analysis period
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".