Consequences of inappropriate criteria for accepting age estimates from otoliths, with a case study for a long-lived tropical reef fish
Bibliographic record
Abstract
Fish ages estimated from increments in otoliths are uncertain because of various sources of error, including increment interpretation. Interpretation error is often addressed by reading each otolith multiple times and accepting age estimates only if readings satisfy certain consistency criteria. Choice of an inappropriate acceptance criterion may significantly bias the accepted age estimates and derived parameters such as mortality. The frequencies and magnitudes of discrepancies from replicate readings of otoliths increased with age for the red bass, Lutjanus bohar. The trend was best described by a constant probability of misinterpreting each increment, indicating an age acceptance criterion that allowed for increasing discrepancy between readings with age. Simulations of three error processes in reading otoliths, two processes of error accumulation within readings, and six acceptance criteria illustrated the biases in age-based metrics that arise from choosing inappropriate acceptance criteria. Biases were largest for static constant, rather than proportional, acceptance criteria, leading to elevated exclusion of older otoliths, overestimation of mortality, and underestimation of mean age. von Bertalanffy growth parameters were generally estimated with little bias. We recommend formal analysis of alternative models of ageing error to choose appropriate acceptance criteria and minimise biases in age-based demographic metrics.
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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 | 0.020 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".