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Record W3012478295 · doi:10.17118/11143/18886

Du réchauffement aux conséquences écologiques : s’adapter, migrer ou disparaître

2019· article· fr· W3012478295 on OpenAlexaffvenue
Limoilou-Amélie Renaud, Mark Vellend, Fanie Pelletier

Bibliographic record

Venue˜Le œclimatoscope · 2019
Typearticle
Languagefr
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsconsAdapter (computing)Computer scienceOperating systemProgramming language

Abstract

fetched live from OpenAlex

Le réchauffement climatique est un phénomène planétaire menant à des changements dans les conditions environnementales, tels que la température et la quantité de précipitations, posant un défi de taille pour les espèces sauvages. De nombreux scientifiques étudient les réponses biologiques des plantes et des animaux face à ces changements. Cet article est un aperçu des connaissances scientifiques sur les stratégies et réponses écologiques que les organismes vivants utilisent pour faire face, notamment, à l’augmentation des températures à l’échelle globale. Toutefois, pour certaines espèces, le réchauffement survient à une vitesse trop rapide, menant à leur disparition, et laissant parfois place à de nouvelles espèces.

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 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.021
GPT teacher head0.252
Teacher spread0.232 · 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 designTheoretical or conceptual
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
Published2019
Admission routes2
Has abstractyes

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