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Record W4319312359 · doi:10.4095/331387

Sixth Generation seismic hazard model of Canada: final input files used to generate the 2020 National Building Code of Canada seismic hazard values

2023· report· en· W4319312359 on OpenAlexaffabout
Michal Kolaj, S Halchuk, J Adams

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsSeismic hazardHazardDocumentationData fileCode (set theory)Hazard mapComputer scienceEngineeringDatabaseSeismologyGeologyProgramming language

Abstract

fetched live from OpenAlex

The enclosed files provide OpenQuake compatible input files to reproduce the 6th Generation Seismic Hazard Model of Canada (CanadaSHM6) as used to produce the seismic hazard values for the 2020 edition of the National Building Code of Canada (NBCC 2020). Example hazard values at selected localities are included, in order for other users to verify that the model has been implemented as we intended. This report contains minimal technical information on CanadaSHM6; complete documentation will be released as a separate Open File. This report supersedes Open File 8630, and the values derived from the model and the online seismic hazard tool described herein supersede the trial values provided in Open File 8629.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score0.312

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0930.032

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 head0.073
GPT teacher head0.273
Teacher spread0.200 · 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 designSimulation or modeling
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".

Quick stats

Citations9
Published2023
Admission routes2
Has abstractyes

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