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Record W7115697062 · doi:10.3917/geo.1575.0056i

Fanny Lopez, L’ordre électrique (infrastructures énergétiques et territoires) , Métis presses, 2019, 208 pages, 32 €

2019· article· W7115697062 on OpenAlexaboutno aff

Bibliographic record

VenueLa Géographie · 2019
Typearticle
Language
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSet (abstract data type)Identification (biology)Perspective (graphical)Focus (optics)Subject (documents)

Abstract

fetched live from OpenAlex

Îles en projet, les carnets du paysage, n° 35, 2019, Actes Sud et École Nationale Supérieure du Paysage, 28 €.Les îles fascinent : c'est presque un cliché, et le développement touristique a fait exploser la demande, non sans conséquences, à la Olivier Renaud, Errances, Éditions Paulsen 2019, 247 pages, 21 € L'auteur est un philosophe au talent de romancier.Sans doute fallait-il une telle approche pour déchiffrer au fil de pérégrinations démesurées et d'un parcours personnel sinueux la personnalité complexe d'un des plus étonnants voyageurs : Vitus Bering.Un Danois passionné de mer à 15 ans, entré par fascination dans la marine que Pierre le Grand offrait à la Russie dans une Europe septentrionale que la Guerre du nord mettait en ébullition.Un marin dans l'âme au regard lointain, ambitieux, ombrageux, proche des grands et aimé du tsar, mais détestant les courtisans et les politiciens, aventurier mal dans sa peau sociale.Les caractères d'un Magellan ou plus tard d'un Dumont d'Urville, qui en firent comme eux un grand explorateur du monde.Son expérience des navigations lointaines acquises au commerce des Indes le fit choisir pour commander l'expédition voulue par un tsar passionné de géographie vers le Kamtchatka encore flou.L'objectif fixé par Pierre 1 er en fin de

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)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.068
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0680.027

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 head0.009
GPT teacher head0.241
Teacher spread0.232 · 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
GenreReview

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".

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Citations0
Published2019
Admission routes1
Has abstractno

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