JDS_Supplementary_Tables_Touil_et_al._Reticuloruminal_pH_and_Subacute_Ruminal_Acidosis_prediction.pdf
Bibliographic record
Abstract
Low reticuloruminal pH (rpH) for a prolonged period could lead to SARA. This disease negatively impacts cow health and is associated with monetary losses for the dairy industry. The aim of this study was to predict rpH and SARA separately using different machine learning (ML) models applied to Fourier transform infrared spectroscopy (FTIR) spectra obtained from routine DHI milk analysis of individual cows. A total of 107 primiparous and multiparous Holstein cows were selected from 12 commercial farms in Québec, Canada, and their rpH was continuously monitored for 150 days using wireless boluses. In parallel, 2,634 individual milk samples were collected in the morning and afternoon and analyzed to obtain FTIR spectra. After the cleaning process, 1,744 samples remained, evenly divided into 872 morning (a.m.) and 872 afternoon (p.m.) samples. FTIR and rpH data were combined to create three equally balanced datasets for ML model development: one for a.m. samples, one for p.m. samples, and one composed of both a.m. and p.m. samples with 872 samples in each dataset. Various spectra pre-processing methods were evaluated, including using the first derivative of the spectra and filtering with three different sets of spectra. Additionally, different ML algorithms, including partial least squares (PLS), random forest (RF), and gradient boosting (GB), were used to predict rpH and SARA. A total of 36 different models were developed and evaluated for both rpH and SARA prediction. All ML models were assessed using three different cross-validation (CV) methods: nested 10-fold, nested leave-one-farm-out (LOFO), and nested leave-cows-out (LCO) CV. For rpH prediction, the best performance was achieved using nested 10-fold CV with median R² values of 0.26, 0.26, and 0.22 for the a.m., p.m., and a.m./p.m. datasets, respectively. However, these performances were likely overoptimistic as none of the models evaluated using nested LOFO or nested LCO CV obtained R² higher than 0.12. Unlike rpH, SARA prediction accuracies evaluated using nested LOFO (a.m.: 59%, p.m.: 69%, a.m./p.m.: 64%), and nested LCO (a.m.: 67%, p.m.: 66%, a.m./p.m.: 64%) were closer to the nested 10-fold CV. These results indicated that rpH was likely not predictable from FTIR, but SARA can be predicted separately and directly from FTIR with 69% accuracy from routine DHI milk samples of individual cows.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.769 | 0.223 |
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".