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Record W4410729780 · doi:10.21203/rs.3.rs-6681466/v1

Plant range disequilibrium in Europe is shaped more by disturbance than climate change

2025· preprint· en· W4410729780 on OpenAlexaff
Sean E. H. Pang, Robert Buitenwerf, Oliver Baines, Svetlana Aćić, Markus Bernhardt‐Römermann, Idoia Biurrun, Gianmaria Bonari, Hans Henrik Bruun, Chaeho Byun, Eduardo Chacón‐Madrigal, Alessandro Chiarucci, Anh Tuan Dang‐Le, Pieter De Frenne, Arildo S. Dias, Jiří Doležal, Emmanuel Garbolino, Behlül Güler, Nate Hough‐Snee, Jens Kattge, Jonathan Lenoir, Adam R. Martin, Sean T. Michaletz, Vanessa Minden, Akira Mori, Ülo Niinemets, Wolfgang Schmidt, Mária Šibíková, Grzegorz Swacha, Koenraad Van Meerbeek, Jens‐Christian Svenning

Bibliographic record

VenueResearch Square · 2025
Typepreprint
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of British ColumbiaUniversity of Toronto
FundersHORIZON EUROPE Framework ProgrammeEuropean CommissionMinistry of Science and ICT, South KoreaNational Research Foundation of KoreaDanmarks GrundforskningsfondNational Research Foundation
KeywordsDisequilibriumDisturbance (geology)EcologyClimate changeRange (aeronautics)Vegetation (pathology)BiodiversityExtinction (optical mineralogy)Environmental scienceSpecies distributionGeographyPhysical geographyBiologyHabitat

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.086
GPT teacher head0.362
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2025
Admission routes1
Has abstractno

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