A decision matrix to better identify repeatable physiological variation within individuals
Bibliographic record
Abstract
Summary The performance of an individual has remained at the heart of evolutionary biology since the time of Darwin. Physiologists are equally drawn to the implications of individual variation for health and sporting endeavours, and specifically, whether or not a physiological trait is repeatable within an individual. Experimental biologists are especially interested in temporally stable physiological traits that are relevant to an individual’s lifetime fitness for natural selection to act upon. Experimental noise, however, confounds the measurement of such repeatability, even though validated protocols exist for measuring many meaningful physiological performance traits. Missing is a decision matrix that helps distinguish individual variation from experimental noise. We propose a precision-&-repeatability assessment matrix (PRAM) that integrates established assessments of individual variability and repeatability. This matrix places metrics that are more repeatable and precise in the quadrant closest to the origins of Cartesian coordinates; those farthest away are less acceptable in terms of both repeatability and precision. As a case study, PRAM is applied to whole-organism aerobic and non-aerobic metabolic performance metrics from fish that were measured with the same protocols. The analysis illustrates that aerobic metabolic metrics can be more repeatable and precise than non-aerobic ones. Consequently, PRAM helps physiologists to better understand whether the observed variability is due to non-repeatable metrics or true individual variation.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".