JDS_Supplementary_Tables_Touil_et_al._Reticuloruminal_pH_and_Subacute_Ruminal_Acidosis_prediction.pdf
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,977 | 0,038 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».