A new model of growth back-calculation incorporating age effect based on otoliths
Bibliographic record
Abstract
We developed a simple back-calculation model that includes the age effect in which otolith size increases continuously during no-growth periods. To evaluate the validity of our proposed model, we back-calculated the past fish lengths and growth rates using the new model and seven traditional back-calculation models (scale-proportional hypothesis (SPH), body-proportional hypothesis (BPH), FraserLee, biological intercept, nonlinear SPH, nonlinear BPH, and modified Fry) using the otoliths of individually tag-recaptured white-spotted char (Salvelinus leucomaenis). The estimated fish lengths corresponded well to observed fish lengths for simple traditional (SPH, BPH, and FraserLee) and the new models. However, the back-calculated growth rates did not correspond to observed growth rates except for the new model. All previous models had considerable bias; growth rates of slow-growing fish were overestimated. As our model incorporating the age effect did not show such bias, this bias would be attributed to the age effect. Our proposed back-calculation model incorporating the age effect should be useful to estimate past growth rates at the individual level.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".