Specimens of opportunity provide vital information for research and conservation regarding elusive whale species
Bibliographic record
Abstract
Summary Elusive species are challenging to study and conserve because basic elements of their biology may be unknown. Specimens of opportunity provide a means of collecting information on these species and may be critical for elusive species’ conservation. We used snowball sampling to identify Sowerby’s beaked whale (Mesoplodon bidens) specimens in museums and research institutions. Snowball sampling proved highly effective: we located 180 specimens from 24 institutions in North America and Europe, 62 of which were not listed in online collections databases, resulting in the largest collated dataset for this species. Analysis of these data resulted in several new findings for this species, including significant morphological variation between specimens from different collection regions, suggesting the presence of previously unidentified population structuring in this species. These data provide critical information regarding this species and demonstrate the effectiveness of specimens of opportunity for elusive species research and conservation. We recommend other researchers consider snowball sampling when designing research projects utilizing specimens of opportunity. Our results demonstrate the usefulness of snowball sampling and specimens of opportunity to elusive species research and conservation, and the methods of our study can be readily adapted for other 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".