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Record W2058983176 · doi:10.4000/insitu.141

De la Seine au plateau : l’impact de la présence militaire sur l’urbanisme de Melun

2011· article· fr· W2058983176 on OpenAlexaff
Judith Förstel

Bibliographic record

VenueIn Situ · 2011
Typearticle
Languagefr
FieldArts and Humanities
TopicEuropean Political History Analysis
Canadian institutionsCanadian Heritage
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

La présence de l’armée à Melun, préfecture du département de Seine-et-Marne, a eu une incidence non négligeable sur le développement de la ville au XIXe et au XXe siècle. En 1780, un quartier de cavalerie s’est en effet installé dans un ancien couvent, sur la rive gauche de la Seine, et a connu un développement ininterrompu jusqu’à la fin du XIXe siècle, avant d’être transféré au nord de la ville en 1905. L’espace ainsi dégagé en bordure du fleuve fut l’occasion pour la municipalité d’engager un important programme d’aménagement, tandis que la construction d’un vaste quartier militaire en périphérie de la ville entraînait l’urbanisation de nouveaux secteurs.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.027
GPT teacher head0.261
Teacher spread0.235 · 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
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

Citations0
Published2011
Admission routes1
Has abstractyes

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