A new proportionality-based back-calculation approach, which employs traditional forms of growth equations, improves estimates of length at age
Bibliographic record
Abstract
The performance of a new proportionality-based back-calculation approach, describing the relationship among length, otolith size, and age using traditional growth curves and assuming a bivariate distribution of deviations from those curves, was evaluated. Cross-validation was used for six teleost species to compare predictions of expected lengths or otolith sizes at age, given otolith size or length, respectively, with those of other proportionality-based approaches that incorporate age. For four species, and particularly Acanthopagrus butcheri when using a biological intercept, better estimates were produced using the new model than were produced using the regression equations in the other back-calculation approaches. Back-calculated lengths for A. butcheri estimated using this model were more consistent with observed lengths, particularly when employing a biological intercept, than those obtained using other proportionality-based approaches and also a constraint-based approach known to produce reliable estimates. By selecting somatic and otolith growth curves from a suite of alternatives to better describe the relationships among length, otolith size, and age, the new approach is likely to produce more reliable estimates of back-calculated length for other 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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".