Comparison of offspring growth models in Atlantic puffins
Bibliographic record
Abstract
Growth of morphological traits in Atlantic puffin (Fratercula arctica) offspring has typically been characterized by linear models, despite clearly displaying nonlinear patterns. We assessed the fit of six typical avian growth models to measurements of puffling mass, wing length, and tenth primary feather length. Across all three biometrics, the first- and second-best performing models were nonlinear, and the worst performing model was the linear model. Specifically, the preferred model for mass, wing, and tenth primary growth was the quadratic model, logistic model, and extreme value function model, respectively. The preferred models were used to generate separate growth curves for individual chicks, from which parameter estimates for growth rate, normalized growth rate, inflection value, and asymptotic value can be extracted. These parameter estimates are easily interpretable and comparable between several of the nonlinear models. We recommend the reported models in future studies incorporating metrics of puffling growth, and encourage the use of this methodology to explore nonlinear growth patterns across avian 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".