Understanding different types of repeatability and intra-class correlation for an analysis of biological variation
Bibliographic record
Abstract
Repeatability (more generally known as intraclass correlation) represents an important quantity of interest in many scientific fields. It represents a metric for summarizing variance decomposition to identify sources of variation in an outcome of interest (e.g. organismal traits). The estimation of variance components is often achieved through linear mixed-effect models or their extension, generalized linear mixed-effect models. Here, we review variants of calculating repeatabilities from mixed-effects models for a variety of conditions and applications. We also recommend which variant might be appropriate under what conditions, focusing on behavioural biology/ecology examples. However, the decision is ultimately with the researcher, since it depends upon their research question, and there is no one-size-fits-all solution. We also highlight the importance of the scope of inference, which affects how repeatabilities are used and interpreted. We recommend transparent reporting of statistical results, including all variance components, which are the building blocks of repeatability. This review aims to assist empiricists in choosing an appropriate repeatability variant and interpretation concerning their questions and scope of inference.
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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 | 0.049 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".