Mixed effects models for fish growth
Bibliographic record
Abstract
Fish growth in a particular year has both intrinsic and environmental components. Intrinsic growth can depend on both the age and size of the fish and on particular characteristics of the individual fish. The environmental component is the influence of external conditions such as food supply on the growth increment. In this article, we present mixed-effects models as an alternative to fixed-effects linear models for incremental fish growth used previously in the literature and show how these models overcome many of the shortcomings of the fixed-effects approach. In addition, widely available software allows for fitting these models and for elaboration of them to learn about the effects of additional factors such as temperature, species interactions, management practices, the introduction of an invasive species, or other known environmental variables. Finally, we provide a connection with the more usual modeling of size-attained data through the use of growth functions such as the von Bertalanffy.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.010 |
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".