Otolith mass as a predictor of age in kokanee salmon (<i>Oncorhynchus nerka</i>) from four Colorado reservoirs
Bibliographic record
Abstract
Estimating ages of individuals in fish populations is crucial for determining characteristics necessary to effectively manage sport fisheries. Currently, the most accepted approach for fish age determination is using thin sectioned otoliths for interpretation. This method is labor-intensive, requires extensive training, and subjectively determines age. Several studies have shown that otolith mass increases with age, yet use of otolith mass to determine fish age is relatively underutilized. However, determining fish age using otolith mass requires relatively little training, is relatively nonsubjective, and is faster compared with other aging techniques. We collected kokanee salmon (i.e., landlocked sockeye salmon, Oncorhynchus nerka ) in 2004 from four reservoirs and from 2000 to 2009 in one reservoir to evaluate the efficacy of using otolith mass to determine fish ages. We used a machine learning technique to predict kokanee salmon ages using otolith mass and various other covariates. Our findings suggest this method has potential to substantially reduce time and financial resources required to age fish. We conclude that using otolith mass to determine fish age may represent an efficient and accurate approach for some species.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".