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Record W3164626300 · doi:10.3166/ges.2021.0004

L’avènement de corridors ferroviaires trans-asiatiques : quel levier de développement ?

2021· article· fr· W3164626300 on OpenAlexaff
Frédèric Lasserre, Éric Mottet

Bibliographic record

VenueGéographie Économie Société · 2021
Typearticle
Languagefr
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Depuis 2011, on observe une diversification de liaisons ferroviaires de transport de fret entre la Chine et l'Europe. Cette initiative commerciale s'est rapidement développée : le nombre de trains a connu une augmentation rapide, et les ambitions des partenaires chinois comme européens sont élevées. Certes, le rail dispose d'une part de marché du fret transasiatique encore faible. Les projets de développement de liaisons ferroviaires pour le fret transasiatiques ont cependant connu une relance spectaculaire avec l'avènement du projet Belt and Road Initiative. On assiste à une expansion des volumes, des services proposés, le tout accompagné par la définition et la mise en œuvre de nouvelles infrastructures ferroviaires, en Russie, en Chine, vers l'Asie du Sud-Est, l'Iran ou le Pakistan, dans ce que la Chine présente comme des corridors de développement. Tout à la fois leviers potentiels de développement et actifs de rapprochement stratégiques, comment ces projets de corridors se développent-ils?

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.763
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.023
GPT teacher head0.278
Teacher spread0.254 · 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 teacher head, not a consensus.

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

Citations2
Published2021
Admission routes1
Has abstractyes

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