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Record W2808679939 · doi:10.7202/1047149ar

Présence annuelle de la sauvagine dans le parc marin du Saguenay–Saint-Laurent

2018· article· fr· W2808679939 on OpenAlexafffundvenueabout
Christine Lepage

Bibliographic record

VenueLe Naturaliste canadien · 2018
Typearticle
Languagefr
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsMinistère des Ressources naturelles et des Forêts
FundersUniversité du Québec à Rimouski
KeywordsHumanitiesArtForestryGeography

Abstract

fetched live from OpenAlex

Le Service canadien de la faune d’Environnement et Changement climatique Canada n’effectue aucun relevé régulier de sauvagine dans les limites du parc marin du Saguenay–Saint-Laurent (Québec, Canada) comme tel, mais il dispose néanmoins de données provenant de 2 inventaires plus globaux pouvant aider à dresser un portrait sommaire de sa fréquentation par ce groupe d’oiseaux. La partie du Saguenay comprise dans le parc n’apparaît pas comme un lieu d’importance pour la sauvagine, et ce, à aucun moment de l’année. En revanche, les sections de l’estuaire moyen et maritime du Saint-Laurent sises dans le parc présentent un intérêt certain pour la sauvagine, puisqu’elles sont fréquentées, selon la période de l’année, par des centaines, voire des milliers d’individus pour des durées variables. Certaines espèces ne font qu’y passer en migration, tandis que d’autres y restent pour plusieurs mois : en été, les eiders à duvet (Somateriamollissima) pour la nidification ou des milliers de macreuses (Melanittaspp.) pour la mue; en hiver, les garrots (Bucephala spp.). L’intérêt de la partie estuarienne du parc marin du Saguenay–Saint-Laurent réside vraisemblablement dans les aires d’alimentation et de repos qu’elle offre à la sauvagine.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.000
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 score0.070
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.221
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

Labeled directly by 2 models reading the full record.

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
Published2018
Admission routes4
Has abstractyes

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