Usefulness of Otolith Weight for Estimating Age-Based Life History Metrics of Lake Trout
Bibliographic record
Abstract
Abstract To determine if otolith weight can be used to accurately and precisely estimate age-based life history metrics of Lake Trout Salvelinus namaycush populations, we quantified relationships between Lake Trout otolith age and weight sampled from a wide geographic and size range of North American lakes. Separate nonlinear age–weight models for each of 22 surveys (each year within each area or lake) described 82.8% of variation in the relationship between otolith age and weight, whereas separate age–weight models for each of 13 lakes or areas within lakes (years combined) described only 0.4% less variation. Over all surveys, age increased at an average rate of 1.13 years per milligram increase in otolith weight and the otolith weight–age relationship was significantly nonlinear. Age of individual fish could not be estimated accurately from otolith weight alone, although bias of mean age estimated from mean otolith weight was small, whether using survey-specific or general otolith age–weight relationships. Age at maturity and survival derived from indirect otolith age–weight relationships were estimated with less bias than growth parameters. We conclude that a general model for estimating Lake Trout age from otolith weight may be more useful for maturity and survival analyses that are less susceptible to estimation error of individual (often old) fish than for growth analysis that can be susceptible to estimation error of individual (often old) fish.
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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 | 0.003 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".