Validity of age estimates from muskellunge (<i>Esox masquinongy</i>) fin rays and associated effects on estimates of growth
Bibliographic record
Abstract
Accurate age estimates are critical for understanding life histories of fishes and developing management strategies for fish populations. However, validation of age estimates requires known-age fish, which are often lacking. We used known-age (ages 1–25) muskellunge (Esox masquinongy) to determine the precision and accuracy of age estimates from fin rays. We also determined whether fin location (anal or pelvic), fin ray number, and preparation methods affected accuracy and precision. Lastly, we determined whether von Bertalanffy growth parameters estimated from fin ray ages were similar to parameters estimated from known ages. Precision and accuracy of age estimates from anal and pelvic rays were similar and estimates were relatively precise (coefficient of variation = 8.5%) and accurate (mean absolute difference from known age = 0.85 years) for ages 4–15, but ages were overestimated for younger fish and underestimated for older fish. Growth models based on estimated age were similar to models based on known age. Anal and pelvic rays offer a nonlethal alternative for age estimation of muskellunge ages 4–15 and for producing reliable estimates of growth.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".