Harbour porpoise (<i>Phocoena phocoena</i>) in the North Atlantic: Abundance, removals, and sustainability of removals
Bibliographic record
Abstract
The status of harbour porpoise (Phocoena phocoena) populations in the North Atlantic has raised numerous concerns. Although a number of factors that may be adversely affecting harbour porpoise populations have been identified, focus has been on the impact of removals, primarily due to incidental catches in fishing gear. As a result, considerable efforts have been made to determine the levels and/or impact of bycatch in a number of areas. Unfortunately, many areas remain little studied. Currently, harbour porpoise are listed as threatened or vulnerable in many parts of their range. In order to determine if the current levels of removals are sustainable, information on stock identity and seasonal movements, population parameters, abundance, and the magnitude of removals is required. Although substantial progress has been made to improve our knowledge of these parameters in the last decade, significant gaps still exist. After reviewing the available data for each sub-population in the North Atlantic, it is clear that the information required to assess the status of harbour porpoise populations is still not available for most areas. Attempts have been made to assess the status of harbour porpoise based on trends in sightings or, in areas where information on abundance and bycatch are available, on models using arbitrary criteria and/or theoretical estimates of potential population growth. Detailed case-specific population models have been proposed but are not yet available.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".