Density-dependent habitat selection and the modeling of sperm whale (<i>Physeter macrocephalus</i>) exploitation
Bibliographic record
Abstract
The monitoring and management of sperm whale (Physeter macrocephalus) populations have proved problematic. Studies of living animals indicate that movements are largely determined by resource availability, thus suggesting that density-dependent habitat selection may be a realistic framework within which to study sperm whale populations. A model, in which animals migrate between 2 × 2° squares at rates that depend on relative resource availability, was used to examine the effects of whaling on measures of sperm whale abundance. The model simulated four types of whaling: shore-based whaling, pelagic open-boat whaling by many boats, pelagic whaling by a fleet based around one factory ship, and pelagic whaling by a fleet sequentially exploiting different parts of the study area. Catch per unit effort was found to have little relationship with population size in any part of the study area for shore-based whaling and for pelagic whaling when the study area was sequentially exploited. Thus, in these circumstances, catch per unit effort should not be used as a measure of depletion. To give a reasonable assessment of depletion, visual or acoustic surveys must extend well beyond the areas being exploited.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".