Correlation between L-Lactate Concentrations in Beef Cattle, Obtained Using a Hand-Held Lactate Analyzer and a Lactate Assay Colorimetric Kit
Bibliographic record
Abstract
Lactate is a product of anaerobic glycolysis, used in animal research as an indicator of muscle fatigue. Therefore, it has been used as an indicator of cattle response to long distance transportation. The aim of this study was to assess the relationship of L-lactate concentrations measured using a Lactate Scout+ analyzer and a traditional lactate assay colorimetric kit. Blood samples were collected by venipuncture from 96 steers (Black or Red Angus × Hereford/Simmental and Black or Red Angus × Charolais; 247 ± 38.2 kg BW) prior to loading (LO1) and after 36 h of transport, and prior to reloading and after an additional 4 h of road transportation, and on d 1, 2, 3, 5, 14, and 28 after transport. The Lactate Scout+ analyzer strip was dipped in blood at the time of sampling, while blood samples were collected into sodium fluoride tubes for use in the colorimetric analysis. Pearson correlations were calculated to assess the strength of the relationship between the experimental methods for the quantification of L-lactate concentrations. The magnitude and direction of the correlation, and the level of statistical significance varied over the observed time points, ranging from r = −0.03 (p = 0.75; LO1) to r = 0.75 (p < 0.0001; d 3). The correlation for the pooled data was weak but statistically significant (r = 0.33, p < 0.0001). Based on the low magnitude of the correlation due to variability across sampling time points in this study, the Lactate Scout+ analyzer is not a suitable alternative to a lab-based assay (considered the gold standard) for measuring L-lactate in transported cattle.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".