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Record W3199636350 · doi:10.1139/cjce-2020-0610

Probability-based static truck loading model for rural bridges in Saskatchewan

2021· article· en· W3199636350 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicTransport Systems and Technology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTruckAxleTrailerWeigh in motionAxle loadAutomotive engineeringEngineeringEnvironmental scienceComputer scienceTransport engineeringStructural engineering

Abstract

fetched live from OpenAlex

A study was conducted to establish a new truck load model intended for the evaluation and design of bridges with simple spans of 20 m or less located on rural roads in Saskatchewan. Monte Carlo simulation was used to generate truck data sets based on site-specific traffic conditions determined from a traffic count program conducted between 2008 and 2012 across all 296 rural municipalities, and data collected from six weigh-in-motion stations in the province from January to December 2013. All axle weights and spacings were modelled as probabilistic parameters. The critical truck configuration featured a truck tractor with a steering axle and tandem axle group, and a truck trailer with a tridem axle group. Truck models with a common axle configuration but varying weights were developed for various reference periods that reliably reproduced extreme nominal load effects over those periods. The use of other data sets may lead to different results.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.180
Teacher spread0.168 · 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