Future Directions in Research on Beaked Whales
Bibliographic record
Abstract
Until the 1990s, beaked whales were one of the least understood groups of large mammals. Information on northern bottlenose whales (Hyperoodon ampullatus) and Baird’s beaked whales (Berardius bairdii) was available from data collected during whaling, however, little information existed on the smaller species other than occasional data gleaned from beach-cast animals. Recent research advances have been plentiful. Increasing global survey effort, together with morphometric and genetic analyses have shown at least 22 species in this group. Longitudinal field studies of at least four species (H. ampullatus, B. bairdii, Ziphius cavirostris, Mesoplodon densirostris) have become established over the last three decades. Several long-term studies support photo-identification catalogues providing insights into life history, social structure and population size. Tag-based efforts looking at diving, movements and acoustics have provided detail on individual behaviour as well as population structure and ranges. Passive acoustic monitoring has allowed long-term and seasonal monitoring of populations. Genetic studies have uncovered cryptic species and revealed contrasting patterns of genetic diversity and connectivity amongst the few species examined. Conservation concern for these species was sparked by mass strandings coincident with military mid-frequency sonar use. Fat and gas emboli have been symptomatic indicators of mortalities related to sonar exposure, suggesting that their vulnerability stems from the physiological exertion of extreme diving for medium-sized whales. Behavioural response experiments have now shown that beaked whales appear to cease foraging and delay their return to foraging and/or leave the area in association with exposure to mid-frequency signals at low acoustic levels. Future priorities for these species will be to (1) continue field-studies to better understand smaller-scale habitat use, vital rates and social structure; (2) develop better detection methods for larger-scale survey work; (3) improve methodology for monitoring energetics, individual body condition and health; (4) develop tools to better understand physiology; (5) use recent genetic advances with improved sample databanks to re-examine global and local beaked whale relationships; (6) further quantify anthropogenic impacts (both sonar and other noise) and their population consequences (7) apply acquired data for realistic mitigation of sonar and other anthropogenic impacts for beaked whale conservation.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.057 | 0.011 |
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".