Notice bibliographique
Résumé
Small bats hibernating in humid areas of North America are in big trouble as a result of deadly white-nose syndrome. This fungal infection, caused by cold-growing fungus Pseudogymnoascus destructans, is now one of the fastest spreading wildlife diseases. Researchers have acted quickly to understand this disease and have already identified that the fungus infects the skin of bats during cold-weather hibernation. The infection causes bats to arouse from hibernation more frequently than uninfected bats. These metabolically costly arousals make bats consume their over-wintering energy stores more rapidly, causing them to die from emaciation and starvation. The true puzzle is that white-nose syndrome does not affect all bats equally. Certain bat species in North America are suffering high death rates, while others only experience mild or no mortality, and bat species from Europe appear to survive hibernation even with the infection.Pseudogymnoascus destructans has already killed over a million bats in North America since its accidental introduction in 2007, and researchers are struggling to identify what traits of the fungus (the pathogen), the affected bat species (the hosts) and the hibernaculum (the environment) create the ‘perfect storm’ of circumstances to increase bat mortality. David Hayman of the Hopkirk Research Institute at Massey University, New Zealand, and his colleagues wanted to identify the factors that leave some species unaffected, while others quickly perish.The team assembled information about the habitat and hibernation conditions of two North American bat species (the highly impacted little brown bat and the less impacted big brown bat) and two European bat species of similar sizes (the serotine bat and the greater mouse-eared bat), which they then incorporated into a computational model to predict fungal growth over a range of bat body temperatures in environments with different humidities. They added this information to models that calculate bat metabolism at various hibernating temperatures to ascertain how the bats consume their energy reserves. They were then able to predict bat survival over a range of hibernation durations and habitats that they are known to occupy.The models predicted that bats with small body sizes – similar to those of the little brown bat – that hibernate in more humid and warm caves would succumb to the disease faster and more often than larger bat species from drier caves. They revealed that the fungus grows fastest in humid conditions and that smaller bats, which have fewer energy reserves to waste on frequent arousals, will be most affected by the disease. In fact, the models were impressively accurate, reproducing the pattern of mortality seen in North America (high to low mortality) and Europe (low to no mortality).Together, Hayman and his colleagues have solved an important part of the white-nose syndrome puzzle, showing that the humidity of the hibernating environment is an important determinant of disease progression. They conclude that their results present ‘a bleak picture’ for small-bodied North American bats. More broadly, they show that understanding interactions between the disease triad – the pathogen, the host and the environment – will help us to quickly understand other deadly diseases that are spreading rapidly.
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 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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,009 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,022 | 0,003 |
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 ».