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Record W7034094317

ROOSTING BEHAVIOR, HABITAT USE, AND RELATIVE ABUNDANCE OF THE NORTHERN LONG-EARED BAT (<em>MYOTIS SEPTENTRIONALIS</em>) FOLLOWING ARRIVAL OF WHITE-NOSE SYNDROME TO MAMMOTH CAVE NATIONAL PARK

2017· article· en· W7034094317 on OpenAlexaboutno aff

Bibliographic record

VenueUKnowledge (University of Kentucky) · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicFolklore, Mythology, and Literature Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatNational parkAbundance (ecology)CavePopulationRelative species abundanceMetapopulationMammothMyotis lucifugus
DOInot available

Abstract

fetched live from OpenAlex

White-Nose Syndrome (WNS; Pseudogymnoascus destructans) is responsible for the regional population collapse of many cave-hibernating bat species, including the northern long-eared bat (Myotis septentrionalis), in eastern United States and Canada. I evaluated roosting behavior, habitat selection, and landscape-scale distribution of roosts of the northern long-eared bat during spring emergence and the early maternity season in Mammoth Cave National Park, Kentucky, USA, from 2015 to 2016. Logistic regression analysis comparing habitat features of roosts with random plots indicated selection of roosts reflected the costs of energetic demands by sex and reproductive status. Relative abundance of local bat species was assessed pre- and post-arrival of WNS in the Park during the summer season, with capture rates observed during the progression of WNS indicating that the fungal disease led to declines in the overall abundance of several bat species on the summer landscape, especially the northern long-eared bat. Distributional trends were quantified using spatial point pattern analysis which indicated that bats had clear roosting patterns associated with landscape level features and habitat resources. Monitoring bat populations regionally and at local scales will be imperative to helping conservation efforts for several bat species most affected by WNS.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.222
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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