THE USE OF NATURAL MARKINGS IN STUDIES OF LONG‐FINNED PILOT WHALES (<i>GLOBICEPHALA MELAS</i>)
Bibliographic record
Abstract
Abstract Photo‐identification using natural markings has been used for pilot whale (Globicephala melas) studies. However, none of these studies investigated the reliability of the marks used. To identify which mark types are reliable and which could improve the method, fifteen mark types, and their distribution within the population, were described. The rates of gain and loss of each mark type were calculated and the variability in visibility was investigated. Although the mark types associated with the current photo‐identification method, the notch and the protruding piece, appear to be permanent, they allowed us to identify only 33% of our sample. The prevalence of all but two mark types is independent of the identifiability of a photograph. One of these is already used in the current photo‐identification method. This independence indicates that the proportion of the population that is currently identifiable does not differ from the rest of the population in its susceptibility to factors causing marks, such as predation, and thus appears to be representative of the whole population. Using saddle patches in combination with the current photo‐identification method would double the percentage of the identifiable individuals. However, due to limitations of matching software, the current method is easier to use.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".