Valid oxygen uptake measurements: using high <i>r</i><sup>2</sup> values with good intentions can bias upward the determination of standard metabolic rate
Bibliographic record
Abstract
Abstract This analysis shows good intentions in the selection of valid and precise oxygen uptake ( O2) measurements by retaining only slopes of declining dissolved oxygen level in a respirometer that have very high values of the coefficient of determination, r2, are not always successful at excluding nonlinear slopes. Much worse, by potentially removing linear slopes that have low r2 only because of a low signal‐to‐noise ratio, this procedure can overestimate the calculation of standard metabolic rate (SMR) of the fish. To remedy this possibility, a few simple diagnostic tools are demonstrated to assess the appropriateness of a given minimum acceptable r2, such as calculating the proportion of rejected O2 determinations, producing a histogram of the r2 values and a plot of r2 as a function of O2. The authors offer solutions for cases when many linear slopes have low r2. The least satisfactory but easiest to implement is lowering the minimum acceptable r2. More satisfactory solutions involve processing (smoothing) the raw signal of dissolved oxygen as a function of time to improve the signal‐to‐noise ratio and the r2s.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".