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Record W3111838862

Le monstre du lac Baker

2017· book· fr· W3111838862 on OpenAlexaboutno aff
Denis M. Boucher

Bibliographic record

VenueBouton d'or Acadie eBooks · 2017
Typebook
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt
DOInot available

Abstract

fetched live from OpenAlex

Tout comme le Loch Ness ou le lac Memphremagog, le lac Baker du Haut Madawaska, serait-il infeste par un monstre? Les trois mousquetaires acadiens plongent dans leur premiere enquete! Trois jeunes ecoliers acadiens (et leur chien) se cherchant une occupation peu banale decident de fonder une agence de detectives et se baptisent Les trois mousquetaires, puisqu'ils sont quatre?! Au meme moment, les riverains du lac Baker, dans le nord-ouest du Nouveau-Brunswick, a la frontiere avec le Quebec et les Etats-Unis sont effrayes par la presence d'un monstre marin. Il n'en faut pas plus pour que Ania, Mamadou et Gabriel plongent au c?ur du mystere. Qu'est-ce qui se cache dans ce lac?? Ce premier de sept romans jeunesse tres populaires en Acadie a ete completement revisite par l'auteur, et les illustrations de Paul Roux ajoutent encore davantage a l'intrigue, l'action et l'humour incontestable de la serie.

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: Other
Teacher disagreement score0.254
Threshold uncertainty score0.510

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.008
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0220.004

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 head0.026
GPT teacher head0.243
Teacher spread0.217 · 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
Published2017
Admission routes1
Has abstractyes

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