Using Tetracycline to Evaluate Age Estimation in a Long‐Lived Aquatic Mammal
Bibliographic record
Abstract
ABSTRACT Age estimation is useful for understanding population parameters and, in many vertebrates, relies on the principle that growth layer groups (GLGs) are deposited annually in specific tissues. In Florida manatees ( Trichechus manatus latirostris ), GLGs in earbones are used to estimate age at death. Opportunities to validate the rate of GLG deposition in earbones from manatees >15 years old are rare, yet important for ensuring accurate age estimation across the species’ lifespan. Tetracycline injection is a useful method for validating GLG interpretations, particularly when the exact age of an animal is unknown. Since 1997, we collected earbones from 10 manatees that were ≥13–69 years old at death and had been injected with tetracycline 9–37 years before death. The number of years since injection (YSI) was estimated by photographing earbone cross‐sections under ultraviolet light, measuring the distance between the fluorescent tetracycline mark and earbone edge, processing the earbones to visualize GLGs, superimposing the distance to evaluate the marks’ locations relative to GLGs, and counting the GLGs between the mark and earbone edge. Seven earbones had tetracycline marks, although 2 of the marks were dull or discontinuous. On average, estimated YSI was 5.6 (SD = 7.5) years less than the known YSI; however, the error was nearly always ≤2 years for manatees that had been injected <20 years before death, consistent with an annual rate of GLG deposition at younger ages. Resorption (i.e., bone turnover) that obliterated GLGs was likely why YSI was underestimated in old manatees with longer post‐injection intervals, although we cannot exclude the possibility that GLG deposition rate may slow in old age. We discuss how age, extrinsic stressors, life history events, and laboratory processing may affect tetracycline visibility, earbone growth, and GLG interpretation. Our study reinforces the challenges with accurately estimating the age of old individuals in long‐lived mammalian 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".