Table 1 in Disease ecology of bats- - the Canadian scene
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.069 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".