Navigating the unknown: assessing anthropogenic threats to beaked whales, family Ziphiidae
Bibliographic record
Abstract
This review comprehensively evaluates the impacts of anthropogenic threats on beaked whales (Ziphiidae)-a taxonomic group characterized by cryptic biology, deep dives and remote offshore habitat, which have challenged direct scientific observation. By synthesizing information published in peer-reviewed studies and grey literature, we identified available evidence of impacts across 14 threats for each Ziphiidae species. Threats were assessed based on their pathways of effects on individuals, revealing many gaps in scientific understanding of the risks faced by beaked whales. By applying a comprehensive taxon-level analysis, we found evidence that all beaked whale species are affected by multiple stressors, with climate change, entanglement and plastic pollution being the most common threats documented across beaked whale species. Threats assessed as having a serious impact on individuals included whaling, military sonar, entanglement, depredation, vessel strikes, plastics and oil spills. This review emphasizes the urgent need for targeted research to address a range of uncertainties, including cumulative and population-level impacts. Understanding the evidence and pathways of the effects of stressors on individuals can support future assessments, guide practical mitigation strategies and advance current understanding of anthropogenic impacts on rare and elusive marine species.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".