Critical factors for the recovery of marine mammals
Bibliographic record
Abstract
{en} Over the past decades, much research has focused on understanding the critical factors for marine extinctions with the aim of preventing further species losses in the oceans. Although conservation and management strategies are enabling several species and populations to recover, others remain at low abundance levels or continue to decline. To understand these discrepancies, we used a published database on abundance trends of 137 populations of marine mammals worldwide and compiled data on 28 potentially critical factors for recovery. We then applied random forests and additive mixed models to determine which intrinsic and extrinsic factors are critical for the recovery of marine mammals. A mix of life-history characteristics, ecological traits, phylogenetic relatedness, population size, geographic range, human impacts, and management efforts explained why populations recovered or not. Consistently, species with lower age at maturity and intermediate habitat area were more likely to recover, which is consistent with life-history and ecological theory. Body size, trophic level, social interactions, dominant habitat, ocean basin, and habitat disturbance also explained some differences in recovery patterns. Overall, a variety of intrinsic and extrinsic factors were important for species' recovery, pointing to cumulative effects. Our results provide insight for improving conservation and management strategies to enhance recoveries in the future.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".