Record books do not capture population trends in horn length of bighorn sheep
Bibliographic record
Abstract
ABSTRACT Many agencies and researchers use data from harvested animals to study temporal trends in phenotype. For large mammals, complete harvest records are typically only available for the past few decades, but records of the largest trophies have been collected for over a century. To examine whether record books and data from male bighorn sheep ( Ovis canadensis ) harvested under a minimum‐curl regulation could detect temporal trends in horn length, we simulated populations of trophy‐harvested male bighorn sheep where horn length was modeled to increase, remain stable, and decrease over time. All populations experienced a simulated harvest based on a minimum horn length, but only horns in the longest 5% of the initial distribution were entered in a fictional record book. We then assessed whether monitoring of harvested and “record” males detected temporal trends. Data from selective harvest underestimated declines and initially underestimated increases, but qualitatively detected both trends. Record‐book entries, however, severely underestimated increases and did not detect declines, suggesting that they should not be used to monitor population trends. When these biases are taken into account, complete trophy harvest records can provide useful biological information. © 2015 The Wildlife Society.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".