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Record W2030731351 · doi:10.1139/z10-011

Dietary response of Barn Owls (Tyto alba) to large variations in populations of common voles (Microtus arvalis) and European water voles (Arvicola terrestris)

2010· article· en· W2030731351 on OpenAlexvenueno aff
Nadine Bernard, Dominique Michelat, Francis Raoul, Jean‐Pierre Quéré, Pierre Delattre, Patrick Giraudoux

Bibliographic record

VenueCanadian Journal of Zoology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTytoMicrotusBiologyShrewSorexEcologyPopulationBarn-owlPredatorZoologyPredationMuridae

Abstract

fetched live from OpenAlex

The diet of the Barn Owl ( Tyto alba (Scopoli, 1769)) was studied over an 8-year period in the Jura mountains of France, during two population surges of its main rodent prey (common voles, Microtus arvalis (Pallas, 1778), and European water voles, Arvicola terrestris (L.,1758)), allowing us to test whether T. alba is an opportunistic predator as is often cited in the literature or exhibits more complex patterns of prey selection as is reported in arid environments. Small mammals were sampled by trapping and index methods. We observed (i) significant correlations between the proportions of A. terrestris, M. arvalis, and woodland rodents in the diet and their respective densities in the field; (ii) interactions between populations of A. terrestris and M. arvalis, indicating that the proportion of each species in diet was affected by the density of the other; (iii) proportions of red-toothed shrews (genus Sorex (L., 1758)) in the diet did not correlate with their abundance in the field, indicating that those species were likely to be preyed upon when others were no longer available. This confirms that T. alba is generally opportunistic; however, prey selection of a focal species (e.g., Sorex spp., grassland species) can be affected by the density or availability of the other prey species.

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)0.001
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 score0.546
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.250
Teacher spread0.233 · 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

Citations48
Published2010
Admission routes1
Has abstractyes

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