Table 1 in Disease ecology of bats- - the Canadian scene
Notice bibliographique
Résumé
Table 1. Key knowledge gaps to target in future research on Canadian bats, with suggestions for approaches to address each set of questions and examples of previous studies relevant to each. Knowledge gapPotential approachesSelected examplesPathogen diversity and drivers of pathogen prevalence in Canadian batsIdentification of previously undescribed pathogensMisra et al. 2009; Subudhi et al. 2018Targeted surveillance of bats and ectoparasites for known pathogens, ideally with spatially and temporally representative sampling within speciesBanerjee et al. 2020; Kotwa et al. 2022Comparative analyses of pathogen diversity and seasonal trends in prevalence among species with diverse behavioursWebber et al. 2017; Guy et al. 2020Comparison of pathogen diversity or prevalence between regional and long-distance migrantsKlug et al. 2011Host switching/sharing of pathogens among bat speciesComparable pathogen sampling across speciesBecker et al. 2021 bDisease in bats——clinical outcomes of infectionCharacterization of clinical signs of disease when observed or following experimental infectionMcGuire et al. 2016Experimental infections to characterize effects of known pathogensDavis et al. 2005; Warnecke et al. 2012; Hall et al. 2021Ecological, physiological, and molecular variation in disease susceptibility and host–pathogen interactions among speciesDavy et al. 2020; Haase et al. 2021; Rogers et al. 2022Disease in bats——impacts on population growth and viabilityLong-term population monitoring to assess impacts of particular diseases on bat abundance and to compare population-level impacts of disease among speciesBalzer et al. 2021; Vanderwolf and McAlpine 2021Evaluation of conservation tools to mitigate disease impacts in species of conservation concernCheng et al. 2016; Davy et al. 2016; Fletcher et al. 2020Occurrence of co-infections and impacts on disease severityComparative, longitudinal surveillance for multiple pathogens within populationsDietrich et al. 2015Experimental co-infections to assess impacts on disease outcomesDavy et al. 2018Studies designed to detect multiple pathogens from sampled bats rather than single-pathogen approachesClare et al. 2019; Neely et al. 2021; Kotwa et al. 2022Role of habitat quality and anthropogenic land cover change on pathogen dynamics and bat healthComparisons of loads/prevalence in fragmented/degraded vs. intact/high-quality habitatsCottontail et al. 2009; Kessler et al. 2018Comparisons of fitness or of pathogen loads/prevalence for cavity-roosting bats in buildings vs. natural structuresLausen and Barclay 2006Transmission dynamics: pathways of exposure and infectionStudies of bat exposure to vector species, including ectoparasitesTalbot et al. 2017Studies sampling vectors for pathogensBanerjee et al. 2020Transmission dynamics: infection/re-infection rates and phenologyLongitudinal studies (resampling individuals and colonies over time, with collection of demographic and ecological/environmental metadata)Becker et al. 2021 aIncorporation of social structure in models of disease transmissionWebber et al. 2017Effects of environmental contaminants on health and disease susceptibility of batsQuantification of bat exposure to pollutantsHickey et al. 2001; Chételat et al. 2018Studies associating contaminant exposure with immune response, pathogen load, or other negative impacts on healthBecker et al. 2021 b; Sandoval-Herrera et al. 2022Risk of spillover and spill back of pathogens among humans, livestock, other wildlife, and batsMulti-species surveillance for potential pathogen spillover from bats to livestock in agricultural areasBecker et al. 2021 aSocial aspects of disease transmission in batsLongitudinal studies (resampling individuals and colonies over time), incorporating relatedness and social network analysesWebber et al. 2016Research connecting seasonal habitat types (e.g., maternity and swarming sites) and investigating social facilitation of migrationEffects of climate change on bat health, including pathogen prevalence and disease severityStudies testing or predicting the effects of shifting weather regimes associated with climate change on habitat quality, prey availability, pathogen transmission, and(or) disease susceptibilityMcClure et al. 2022 Note: Although we have focused on knowledge gaps for Canadian species, examples include studies addressing these gaps elsewhere. Examples are not exhaustive but are intended to illustrate effective approaches.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,006 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,069 | 0,006 |
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 ».