The ethological shortfall: case study of an endangered shorebird
Bibliographic record
Abstract
Abstract Poor knowledge of animal behaviour impedes understanding of ecology and evolution and reduces human appreciation of the natural world. We call this the ‘ethological shortfall’, parallel to Linnean and other knowledge shortfalls in conservation biology and systematics. We analysed sound recordings of breeding spoon-billed sandpipers (Scolopacidae: Calidris pygmaea ), a critically endangered species. Sixteen years of field research, and a focused short-term study, provided material for our study. All the species’ calls are unique within its clade; hence our findings have immediate practical use for detecting individuals within the breeding period. No sound recordings exist for the lengthy non-breeding period, when most anthropogenic impacts occur. This gap needs to be filled, so that inventories and automated detection can be conducted in that period. We discovered information that is new and has scientific and practical applications at both the species and higher taxonomic levels (e.g., species-specificity of brief ‘alarm’ notes). We conclude that a useful account of endangered species’ behaviour can be obtained through first-hand knowledge of natural history, a research plan based on knowledge of related species, and targeted sampling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".