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Record W7033025354

Originalités biogéographiques et atlas de la faune sauvage du Nunavik (Québec, Canada)

2023· dissertation· fr· W7033025354 on OpenAlexaboutno aff

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2023
Typedissertation
Languagefr
FieldArts and Humanities
TopicLiterature and Cultural Memory
Canadian institutionsnot available
Fundersnot available
KeywordsTundraArcticThe arcticRange (aeronautics)Global biodiversityFauna
DOInot available

Abstract

fetched live from OpenAlex

This work focuses on the mammals and birds of Nunavik. This is the northernmost region of Quebec, beyond the 55th parallel north. This area is little known to naturalists, unlike other more northerly or southerly territories equipped with scientific study stations. The interest of this study lies in the lack of knowledge of the region's wildlife. The aim of this work is to determine the biogeographical originalities of Nunavik from a faunistic point of view in comparison with the rest of North America, but also to find out which species are present on the territory and their distribution.It would appear that Nunavik is a territory of transition between arctic and forest species. In terms of species distribution, at least 156 species are thought to inhabit the region, most of them in the southern half of Nunavik. Extreme climatic conditions at high latitudes and the absence of forest cover limit the expansion of all species over the entire territory. In addition, 79% of species appear to be reaching the limits of their range on the territory. This reinforces our concern that faunal assemblages could be completely overhauled by the end of the century. In other words, the disappearance of traditional arctic species in favor of forest species, to the detriment of (sub)arctic tundra 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 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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.011
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0270.003

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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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