Lactate minimum underestimates the maximal lactate steady-state in swimming mice
Bibliographic record
Abstract
The intensity of lactate minimum (LM) has presented a good estimate of the intensity of maximal lactate steady-state (MLSS); however, this relationship has not yet been verified in the mouse model. We proposed validating the LM protocol for swimming mice by investigating the relationship among intensities of LM and MLSS as well as differences between sexes, in terms of aerobic capacity. Nineteen mice (male: 10, female: 9) were submitted to the evaluation protocols for LM and MLSS. The LM protocol consisted of hyperlactatemia induction (30 s exercise (13% body mass (bm)), 30 s resting pause and exhaustive exercise (13% bm), 9 min resting pause and incremental test). The LM underestimated MLSS (mice: 17.6%; male: 13.5%; female: 21.6%). Pearson's analysis showed a strong correlation among intensities of MLSS and LM (male (r = 0.67, p = 0.033); female (r = 0.86, p = 0.003)), but without agreement between protocols. The Bland-Altman analysis showed that bias was higher for females (1.5 (0.98) % bm; mean (MLSS and LM): 4.4%-6.4% bm) as compared with males (0.84 (1.24) % bm; mean (MLSS and LM): 4.5%-7.5% bm). The error associated with the estimated of intensity for males was lower when compared with the range of means for MLSS and LM. Therefore, the LM test could be used to determine individual aerobic intensity for males (considering the bias) but not females. Furthermore, the females supported higher intensities than the males. The differences in body mass between sexes could not explain the higher intensities supported by the females.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".