Mismeasure of secondary sexual traits: an example with horn growth in the <scp>I</scp>berian ibex
Bibliographic record
Abstract
Abstract Monitoring programmes and studies focused on secondary sexual characters (SSCs) depend on the accuracy of measurements. However, methods of measurements of SSC, such as horns of ungulates, vary throughout the literature. Thus, the accuracy of horn growth measurements as proxies of true horn growth and the comparability of results inferred from different horn growth measurements may be questionable. We used the horns of Iberian ibex Capra pyrenaica to compare horn growth measurements and to analyse reliability with true horn growth. Our results reveal that measurements used in previous studies differed substantially from true horn growth and volume estimated as a barrel appeared as the best proxy of annular segments of horns in the Iberian ibex. Horn growth measurements are not necessarily mutually comparable, just as classical measurements are not necessarily representative of true horn growth. We discuss the wider implications of these results and suggest that biological processes linked to horns of ungulates should be reappraised using improved and accurate measurements because horn growth pattern is a key factor in sustainable management and conservation plans of ungulate species around the world.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".