An approach to estimate short‐term, long‐term and reaction norm repeatability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Evolutionary ecologists increasingly study reaction norms that are expressed repeatedly within the same individual's lifetime. For example, foragers continuously alter anti‐predator vigilance in response to moment‐to‐moment changes in predation risk. Variation in this form of plasticity occurs both among and within individuals. Among‐individual variation in plasticity (individual by environment interaction or I × E ) is commonly studied; by contrast, despite increasing interest in its evolution and ecology, within‐individual variation in phenotypic plasticity is not. We outline a study design based on repeated measures and a multilevel extension of random regression models that enables quantification of variation in reaction norms at different hierarchical levels (such as among and within individuals). The approach enables the calculation of repeatability of reaction norm intercepts (average phenotype) and slopes (level of phenotypic plasticity); these indices are not specific to measurement or scaling and are readily comparable across data sets. The proposed study design also enables calculation of repeatability at different temporal scales (such as short‐ and long‐term repeatability), thereby answering calls for the development of approaches enabling scale‐dependent repeatability calculations. We introduce a simulation package in the R statistical language to assess power, imprecision and bias for multilevel random regression that may be utilised for realistic data sets (unequal sample sizes across individuals, missing data, etc). We apply the idea to a worked example to illustrate its utility. We conclude that consideration of multilevel variation in reaction norms deepens our understanding of the hierarchical structuring of labile characters and helps reveal the biology in heterogeneous patterns of within‐individual variance that would otherwise remain ‘unexplained’ residual variance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it