66 A novel analytical method for identifying periods of increased mortality in individual wean-to-finish pig barns
Notice bibliographique
Résumé
Abstract Mortality rates in all phases of pork production have increased over the past several years. Decreasing mortality rate is of utmost economic importance to producers, especially in weaning-to-finishing pigs where additional investment in feed, medications, and other inputs are expended. A novel analytical method was used to identify sequences of days of increased mortality, defined as mortality episodes, within cohorts of weaning-to-finishing pigs. Cubic smoothing spline regression equations were estimated between days post placement (DPP) and mortality (total daily number of dead and euthanized pigs) for 42 groups of pigs placed in 12 rooms within 6 barns (2 rooms per barn) in Illinois and Iowa from December 2020 to July 2022 (n = 5,676 observation days). For each group, the instantaneous slope between DPP and mortality from spline regression was calculated and used to identify changepoints [i.e., peaks (negative instantaneous slope at DPPt and positive instantaneous slope at DPPt-1) and valleys (positive instantaneous slope at DPPt and negative instantaneous slope at DPPt-1)]. Within each mortality sequence, the maximum and minimum instantaneous slope were considered the start and end of the mortality episode, respectively. Mortality episodes were only considered high mortality episodes if the following metrics were satisfied: 1) greater than or equal to 1 mortality per day, or 2) greater than or equal to 1/3 of the days in the mortality sequence had at least 1 mortality. Days were classified as “start days” if they were within 1 d from the maximum instantaneous slope; likewise, we classified a day as “end days” if it was 1 d from the minimum instantaneous slope. Any day in between the start and end of a mortality episode were defined as “peak days”, and a “normal day” was all other days that did not fall within the start, peak, or end of a high mortality episode. Least-squares means for predicted mortality from a negative binomial generalized linear model showed that mortality was highest and lowest for peak days and normal days, respectively (3.475 ± 0.1729 and 0.591 ± 0.0156 pigs, respectively; P < 0.05; Table 1). Mortality during start or end days of an episode did not differ from each other (2.185 ± 0.1973 and 2.069 ± 0.1881 pigs, respectively; P > 0.05; Table 1) but were significantly greater and lower than a normal or peak day, respectively (P < 0.05; Table 1). The proposed analytical method provides more precise classification of mortality episodes in pig cohorts than previously proposed methods. Results from this method can be used in models to predict the start of mortality episodes, providing ample time for intervention to reduce the overall number of dead pigs in wean-to-finish pig production.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».