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Record W4416772205 · doi:10.1007/s10344-025-02029-9

Shifting ranges: climate influence on the forest dormouse (Dryomys nitedula) distribution in Poland

2025· article· en· W4416772205 on OpenAlexaff
Jan Cichocki, Łukasz Walas, Agnieszka Ważna, Grzegorz Lesiński, Grzegorz Błachowski, Marcin Brzeziński, Sven Büchner, Mateusz Ciepliński, Tomasz Figarski, Iwona Gottfried, Tomasz Gottfried, Grzegorz Hebda, Mirosław Jurczyszyn, Paweł Kmiecik, Tomasz Lamorski, Zbigniew Mierczak, Barbara Pregler, Jarosław Rabiasz, Mateusz Srebrny, Maria Sobczuk, Michał Stopczyński, Agnieszka Suchecka, Marcin Warchałowski, Błażej Wojtowicz, Tomasz Zwijacz‐Kozica

Bibliographic record

VenueEuropean Journal of Wildlife Research · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsCanadian Association of Occupational Therapists
Fundersnot available
KeywordsDeforestation (computer science)Range (aeronautics)Distribution (mathematics)Climate changePopulationSpecies distributionGlobal warming

Abstract

fetched live from OpenAlex

The northern limit of the geographic range of the forest dormouse runs through Poland. The distribution of the forest dormouse in Poland is unclear. The presence of the species has been confirmed in 128 localities in Poland. In the literature the forest dormouse is most frequently reported from forest complexes in north–east Poland, especially from the Białowieża Primeval Forest. In other areas, the distribution of the species is mostly patchy. In some areas, e.g. in the Sudetes or in the Bieszczady Mountains, its occurrence is uncertain. The aim of this study was to analyse the distribution of the forest dormouse population in Poland and to assess potential links between distributional changes and climate change. The analysis of climate models shows that an important factor influencing the occurrence of the species in Poland is climate change from continental to Atlantic climate. The historical disappearance of forest dormouse populations can be linked to both deforestation and climate change.

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)0.006
metaresearch head score (Gemma)0.001
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 score0.012
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.332
Teacher spread0.290 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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