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Record W6910320967 · doi:10.4224/40001924

Niveau 1: Outil de sélection préliminaire en fonction des risques sismiques (OSP) pour les bâtiments existants. Partie 2: documentation technique à l'appui

2020· report· fr· W6910320967 on OpenAlexaffvenue

Bibliographic record

VenueNPARC · 2020
Typereport
Languagefr
Field
Topic
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsElectrocutionMultispectral ScannerStatistical analysis

Abstract

fetched live from OpenAlex

L'OSP de niveau 1 vise à déterminer rapidement les bâtiments dont le rendement sismique peut être évalué avec une certitude raisonnable sur la base de quatre critères clés, à savoir : (1) la sismicité; (2) l'édition du CNB de référence; (3) le temps d'occupation restant; et (4) les conséquences d'une défaillance. L'OSP de niveau 1 permet également de déterminer les conditions particulières qui déclenchent immédiatement une évaluation plus approfondie des risques sismiques. Ces conditions sont (1) type de bâtiment modèle inconnu, (2) désignation patrimoniale fédérale, (3) changement d'usage qui augmente des charges structurales, (4) conséquences de défaillance supérieures aux conséquences de défaillance initiales, (5) catégorie d'emplacement F (comme les sols liquéfiables), et (6) présence de dangers géologiques.

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)0.004
metaresearch head score (Gemma)0.015
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: Methods · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.007

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 head0.061
GPT teacher head0.336
Teacher spread0.275 · 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
GenreMethods

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
Published2020
Admission routes2
Has abstractyes

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Same venueNPARC→French-language works237,207→