PSVII-3 Correlations between L-lactate concentrations obtained using a handheld lactate analyser and a lactate assay colorimetric kit in beef cattle transported by road
Bibliographic record
Abstract
Abstract Blood metabolites are used to assess a variety of animal conditions for veterinary diagnosis and research. Concentration of metabolites in blood can be measured using a commercially-available lab-based assay or in real-time using a handheld device developed to be more time- and cost-effective than the lab-based method. 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 (Lactate Scout, EFK Diagnostics, Barleben, Germany) and a lactate assay colorimetric kit (Lactate Assay Kit, Cell Biolabs Inc., San Diego, CA). Blood samples were collected by venipuncture from 96 steers (245 ± 35.7 kg BW) prior to (L1) and after 36 h, and prior to 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 colorimetric analysis. Pearson correlations were calculated to determine the relationship between the experimental methods for the quantification of L-lactate concentrations. The strengths and levels of statistical significance of the correlation varied over the observed time points, r = -0.03, P = 0.75 (L1) to r = 0.75, P = < 0.0001 (d 3). The correlation for the pooled data was weak but statistically significant (r = 0.33, P < 0.001). Based on the experimental results, the Lactate Scout+ analyzer is not a suitable alternative to a lab-based assay for measuring L-lactate in transported cattle, due to variability across sampling time points and weak correlation with the traditional enzymatic method.
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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 | 0.001 | 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 | 0.001 | 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".