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Record W7058224201

Monographie du Centre local de développement (CLD) des Chutes-de-la-Chaudière

2008· other· fr· W7058224201 on OpenAlexaboutno aff

Bibliographic record

VenueÉrudit documents and data repository (Érudit Consortium, University of Montreal) · 2008
Typeother
Languagefr
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)TelmatologyUrban environment
DOInot available

Abstract

fetched live from OpenAlex

Le présent travail se subdivise essentiellement en trois parties dont voici quelques indications : Première partie : Le contexte d'émergence et l'histoire de la promotion économique dans le territoire des Chutes-de-la-Chaudière Nous présentons d'abord la localisation de la MRC des Chutes-de-la-Chaudière. Nous relatons ensuite les faits historiques entourant la délimitation de ce territoire et nous comparons des données démographiques et socio-économiques avec la région plus vaste qu'est Chaudière-Applaches et avec le Québec. Une description des organismes précédant le CLD des Chutes-de-la-Chaudière est faite par la suite. Enfin, nous abordons brièvement l'implantation des CLD au Québec, en mettant toutefois l'emphase sur celui des Chutes-de-la-Chaudière. Deuxième partie: Le profil du CLD des Chutes-de-la-Chaudière Suite à la description du contexte d'émergence du CLD des Chutes-de-la-Chaudière, la seconde partie de ce travail aborde les dimensions institutionnelle et organisationnelle du CLD des Chutesde- la-Chaudière. Troisième partie: Le bilan et l'avenir du CLD des Chutes-de-la-Chaudière Le cinquième chapitre traite des éléments de synthèse et de bilan. Le chapitre suivant, soit le sixième et dernier chapitre, présente les perspectives anticipées pour les trois prochaines années.

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: Other
Teacher disagreement score0.188
Threshold uncertainty score0.378

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.008
Science and technology studies0.0040.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0310.002

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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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
Published2008
Admission routes1
Has abstractyes

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