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Record W7067065128

Les entreprises forestières autochtones du Québec

2023· other· fr· W7067065128 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeother
Languagefr
FieldEngineering
TopicSlime Mold and Myxomycetes Research
Canadian institutionsnot available
Fundersnot available
KeywordsAccident insurancePoor peoplePortrait
DOInot available

Abstract

fetched live from OpenAlex

Ce rapport présente les résultats d'une enquête menée auprès des entreprises forestières autochtones du Québec (EFAQ). L'enquête visait à dresser le portrait des EFAQ pour en saisir la diversité et connaître leurs motivations, les méthodes de gestion employées et leurs besoins. Elle s'adressait à toutes les entreprises dont les activités dépendent des ressources forestières (bois, faune, paysage); ou qui offrent des produits et services au secteur forestier. Elle couvrait donc plus large que les secteurs traditionnels (récolte, transformation, transport). Un questionnaire a été reçu par 199 entreprises. Le taux de réponse a été de 16%. Soulignons que l'étude est basée sur des entreprises actives seulement.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.065
Threshold uncertainty score0.130

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 studies0.0060.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.029
GPT teacher head0.261
Teacher spread0.233 · 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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