Notice bibliographique
Résumé
Genomic selection is leading to many changes in the artificial insemination (AI) industry in North America, which is now in a transition period. In 2009, 7,894 males and 6,850 Holstein females were genotyped with the Illumina 50K panel, bringing the total number of Holstein animals genotyped to date to 34,323. The short time required to obtain a genomic evaluation on young males more than compensates for their reduced accuracy of evaluation compared to progeny tested bulls, so that theoretically a young bull scheme based on genomic information is more efficient than one based on organized progeny testing. However, several questions remain to answer before AI organizations fully change their breeding strategies. In particular, what will be the producer acceptance of unproven versus proven bulls, how many progeny tested bulls will be required each year to compensate for the loss of prediction accuracy of marker effects over time, and what will be the impact of a decrease in performance recording incentives linked to organized progeny testing on the ability to generate adequate phenotypic data and bull proofs in the future? For the time being, genomic selection has led AI organizations to increase the number of planned matings compared to bulls on the ground, revise contracts with breeders to accommodate the genotyping of progeny from these matings, and collect more embryos from top females. Over all competition has markedly increased for access to these top females. At least one AI organization has been purchasing or leasing females. Top young genotyped bulls are primarily from three well-known proven sires and their sons, which could have a negative impact on the genetic variability of the breed unless new superior sires with different pedigrees are found. Relatively few young bulls were from unproven sires in 2009, but this number will likely increase in 2010. Bulls entering AI may now be used either in progeny testing programs or commercially as unproven bulls. The number of bulls entering AI was similar in 2006, 2007 and 2008. Numbers for 2009 are down for some companies and up for others, but the general trend is a decrease. Data about the relative market share of unproven bulls is not readily available, but individual companies have reported sales ranging from 5% to 40% of their total semen sales. Some of the new genomic tools that will impact the work of dairy cattle breeding organizations in future include low density panels, high density panels and eventually sequencing.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 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 | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 tête enseignante, 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 ».