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Record W4382982668 · doi:10.3390/life13071495

Year-Round Bat Activity and Species Richness Near Temporary Ponds in the Mediterranean Region

2023· article· en· W4382982668 on OpenAlexaff
Ioanna Salvarina, Panagiotis Georgiakakis, Artemis Kafkaletou Diez, Triantafyllia-Maria Perivolioti, Ioanna Vassilaki, Matina C. Kalcounis‐Rueppell

Bibliographic record

VenueLife · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsUniversity of Alberta
FundersHellenic Foundation for Research and Innovation
KeywordsSpecies richnessMediterranean climateGeographyBiologyEcology

Abstract

fetched live from OpenAlex

Mediterranean temporary ponds are recognized as conservation priority habitats that face anthropogenic threats and are important habitats for a number of aquatic and terrestrial animals and plants. Bats are a diverse group of animals that use ponds for drinking and feeding on emerging aquatic insects and terrestrial insects in the riparian zone. We investigated the importance of temporary ponds for bats in Greece by acoustically sampling bat community structures and activity at temporary ponds throughout the year. We sampled monthly, from 3 to 13 months in 2019-2020, at sites at the pond edge and approximately 150-300 m away from the edge, at four temporary ponds in northern and southern Greece. Our results confirm the importance of temporary ponds for bats as activity was recorded year-round and was high in all but the winter months. In general, the distance to the edge of the pond and the presence of water in the pond explained bat activity together with air temperature. Importantly, whether dry or not, all ponds supported bat activity, independent of their particular characteristics. This study highlights the urgent need for the conservation of temporary ponds, especially in areas with limited water availability.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.211

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.073
GPT teacher head0.245
Teacher spread0.173 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2023
Admission routes1
Has abstractyes

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