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Record W2996277960 · doi:10.3917/riges.444.0040

Entretien avec Erik Grab : le chemin collectif de la mobilité durable

2019· article· fr· W2996277960 on OpenAlexvenueaboutno aff
Julie Barlow

Bibliographic record

VenueGestion · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicDiverse multidisciplinary academic research
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesSummitPolitical scienceAnticipation (artificial intelligence)ArtGeographyCartographyComputer science

Abstract

fetched live from OpenAlex

En juin 2019, plus de 5 000 participants ont assisté à la conférence Movin’ On Summit à Montréal. Erik Grab, vice-président à l’anticipation stratégique, à l’innovation et au développement durable pour le Groupe Michelin ainsi que responsable de ce sommet, a accordé une entrevue à Gestion pour parler de mobilité durable, un sujet au cœur des questions abordées par les 300 organismes et entreprises faisant partie du Movin’ On Lab , un regroupement qu’il a créé en 2012 dans la foulée de son activité phare.

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)0.005
metaresearch head score (Gemma)0.008
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: none
Teacher disagreement score0.032
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0090.005
Scholarly communication0.0120.008
Open science0.0010.007
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0210.007

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 head0.018
GPT teacher head0.308
Teacher spread0.290 · 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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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