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Record W2795320130

LES ETABLISSEMENTS EDMOND FROGERAIS

2017· preprint· fr· W2795320130 on OpenAlexaboutno aff
André Frogerais

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2017
Typepreprint
Languagefr
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Edmond FROGERAIS est né en 1880 à Laval (Mayenne), sa famille était originaire de Brest (Finistère). Comme beaucoup de bretons, Il commence sa carrière dans la Marine Nationale qu’il quitte en 1904 avec le grade de Quartier Maitre. Il gagne Paris pour devenir Chef d’Atelier de la société Pouré et Sauton domicilié à Montreuil qui fabrique des mélangeurs pour l’industrie alimentaire, ainsi que des machines alternatives pour fabriquer les comprimés pharmaceutiques. En 1910, le pharmacien Constant David des laboratoires David Rabot installé à Courbevoie le contacte pour la fourniture d’une machine automatique à imprimer les pilules, Edmond Frogerais prépare un projet mais son directeur Charles Pouré ne croit pas à l’avenir de l’Industrie Pharmaceutique et refuse la commande, il préfère s’orienter vers le production de machines à tailler les bouchons de liège qui luisemble plus porteur. Constant David insiste, Edmond Frogerais sait comment fabriquer la machine mais n’en a pas les moyens : Constant David lui propose de le financer et lui avance quelques rouleaux de louis d’or.Il construit la machine dans son garage, rue du gazomètre à Montreuil elle est livrée en fiacre à Courbevoie. C'est le début des établissements Frogerais.

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)0.002
metaresearch head score (Gemma)0.003
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.096
Threshold uncertainty score0.321

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0960.020

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 head0.031
GPT teacher head0.240
Teacher spread0.209 · 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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