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Record W2171129257 · doi:10.1139/z07-139

Small mammals in nests of cavity-nesting birds: Why should ornithologists study rodents?

2008· article· en· W2171129257 on OpenAlexvenueno aff
Dorota Czeszczewik, Wiesław Walankiewicz, Marzena Stańska

Bibliographic record

VenueCanadian Journal of Zoology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNest (protein structural motif)SciurusBiologyApodemusMammalEcologyNest boxPopulationZoologyPredationHabitat

Abstract

fetched live from OpenAlex

We analyze the frequency of occurrence of small mammals recorded in natural cavities and nest boxes in the Białowieża Forest (eastern Poland) and also describe some parameters of tree cavities. A total of 748 cavities and 60 nest boxes in primeval tree stands and 190 nest boxes in managed tree stands were monitored. Both cavities and nest boxes in primeval stands were occupied by mammals less often than nest boxes in managed stands (0.70%, 0.12%, and 1.26%, respectively). The following mammal species were recorded in natural cavities: Eurasian red squirrel ( Sciurus vulgaris L., 1758), forest dormouse ( Dryomys nitedula (Pallas, 1778)), yellow-necked mouse ( Apodemus flavicollis (Melchior, 1834)), and bats (Microchiroptera species). Three mammalian species were recorded in nest boxes: forest dormouse, fat dormouse ( Myoxus glis (L., 1766)), and yellow-necked mouse. The attractiveness of the boxes for mammals increases in managed forests, probably because of a shortage of natural cavities. We suggest that the role of rodents in the breeding ecology of cavity-nesters is underestimated, since studies on natural cavities are rather rare and the species identities of nest predators are most likely poorly recognized. To understand the breeding ecology of birds, ornithologists should study the population dynamics of mammals and the manner in which they use tree cavities and nest boxes.

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 score0.625
Threshold uncertainty score0.980

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.090
GPT teacher head0.275
Teacher spread0.185 · 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

Citations70
Published2008
Admission routes1
Has abstractyes

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