Using mark–recapture information to validate and assess age and growth of long-lived fish species
Bibliographic record
Abstract
Long-lived species from marine and freshwater environments have experienced declines linked to anthropogenic effects such as overexploitation, dam construction, and habitat modification. An understanding of the age structure and the associated dynamics determined from these data for long-lived species is critical for both perseverance of at-risk species and maintenance of exploited species. We used pallid sturgeon (Scaphirhynchus albus) to evaluate the efficacy of mark–recapture data from known-age, hatchery-reared fish (ages 1 to 7) to corroborate age and growth estimates obtained from sectioned pectoral fin rays. Accuracy of age estimates from known-age fish was 13%, whereas 72% of estimates were within 2 years of the true age. Annual growth was significantly different between estimated growth (back-calculated) and actual observations of tagged pallid sturgeon. Age for pallid sturgeon of any given size was estimated with parameters derived from mark–recapture data, and the predicted length-at-age relation was similar to observations from known individuals. In instances where age determination for all ages of interest cannot be verified, mark–recapture appears to be a viable solution for examining growth and has shown promise as a tool for estimating ages in long-lived species with calcified structures that are difficult to read.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".