Modelling seasonal increments in size to determine the onset of annual growth in fishes
Bibliographic record
Abstract
A new method based on modelling of seasonal growth increments (GSI) in total length was found useful for assessing the date of onset of annual growth for 16 fish species in a temperate fluvial lake. Model comparisons indicated that polynomial (linear or quadratic) functions provided better fits to seasonal growth and were more likely to avoid convergence problems than alternative non‐linear models. There was little evidence for differences in the date of onset of growth between years, nor among age classes within individual species. The onset of growth also was to some extent synchronized among species and was concentrated within a narrow temporal window ofc.2 weeks, between 18 May and 2 June, which corresponded to mean water temperatures between 16·1 and 17·3° C. There was no apparent relationship between date of onset and species’ thermal preferenda or thermal preferences. By producing a point estimate along with appropriate 95% CI, theGSImethod provides useful information on the onset of growth and the uncertainty about that estimate. TheGSIanalyses can contribute to a better understanding of environmental influences on the onset of growth and the length of the growing season, and of thermal thresholds for growth, including their use in calculation of degree‐day 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".