Skull measurements as a tool for avian species identification
Bibliographic record
Abstract
Most studies on birds rely on external characteristics, such as plumage, for species identification. However, in many cases, skeletal remains may be the only features available for bird species identification. Our study was designed to understand whether skeletal measurements, specifically cranial measurements, could be used for avian species identification. We hypothesized that like for other taxa (e.g., mammals) skull measurements would be instructive for species identification. We gathered museum specimens representing 12 avian species belonging to three orders: Strigiformes ( N = 63 individual specimens, 5 species), Accipitriformes ( N = 26, 5 species), and Passeriformes ( N = 39, 2 species) collected in Saskatchewan, Canada and measured 15 skull characteristics to aid in species identification. We constructed separate decision tree models for each order and these models correctly classified between 93% and 100% of the training data and between 83% and 100% of the testing data. Our findings support the idea that cranial measurements can be used as a cost effective and accurate tool to identify several closely related species from one geographic location when more reliable identifying features, such as plumage, are not available.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".