Evaluation of Gum‐line Recession for Aging Lynx ( <i>Lynx canadensis</i> )
Bibliographic record
Abstract
ABSTRACT Accurate age estimates of wildlife are important for both research and management purposes. Despite the value of determining the age of individuals, cost effective and minimally‐invasive approaches are lacking for most species. Assessment of gum‐line recession is a minimally‐invasive, cost‐effective method used for aging some felids; however, its reliability is unknown. Here, we evaluate the utility of gum‐line recession for aging Canada lynx ( Lynx canadensis ), using carcasses collected during the winters of 2015–2016 and 2016–2017 from trappers in Yukon, Canada. The purpose of our study was to 1) assess the repeatability of measuring gum‐line recession within and between observers, 2) compare measurements of gum‐line recession to estimates of age obtained from cementum annuli analysis, and 3) test for differences in mean gum‐line recession between age‐classes of lynx (i.e., young, prime adult, and old adults). We found that although estimated gum‐line recession was repeatable within an observer, it was not between observers, and did not correlate strongly with estimated age based on cementum annuli. Significant differences in mean gum‐line recession were found between age classes. We conclude that gum‐line recession does not provide an accurate estimate of age in Canada lynx and that other methods would be required if tooth extraction is not desirable. However, gum‐line recession may be useful for determining age class of lynx. Finally, we recommend similar assessments of precision and observer bias when measuring gum‐line recession to estimate the age of other species. © 2021 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".