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 ( O 2 ) measurements by retaining only slopes of declining dissolved oxygen level in a respirometer that have very high values of the coefficient of determination, r 2 , are not always successful at excluding nonlinear slopes. Much worse, by potentially removing linear slopes that have low r 2 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 r 2 , such as calculating the proportion of rejected O 2 determinations, producing a histogram of the r 2 values and a plot of r 2 as a function of O 2 . The authors offer solutions for cases when many linear slopes have low r 2 . The least satisfactory but easiest to implement is lowering the minimum acceptable r 2 . 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 r 2 s.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".