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Record W2800597253 · doi:10.7939/r3qt5j

Evaluation of Weigh-In-Motion Systems in Alberta

2012· article· en· W2800597253 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUniversity of Alberta Library · 2012
Typearticle
Languageen
FieldEngineering
TopicTransport Systems and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsMotion (physics)GeographyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Weigh-In-Motion (WIM) systems are used for dynamic traffic data collection. These sensors are capable of collecting various truck characteristics such as weights, speed, and dimensions. Alberta Transportation (AT) installed 20 WIM sensors in six different highway sections across Alberta in 2004. The accuracy of these measurements and their effects on pavement design is evaluated in this thesis. To investigate the accuracy of the WIM sensors a verification test was conducted on the sensors from 2004 to 2010. The errors in the WIM sensors’ measurements were estimated. Statistical analysis was performed on the database of errors. Statistical analysis on the verification test program database showed that WIM weight errors do not comply with current standards and there is a need to improve the system. The new predicted pavement performance results from the Mechanistic Empirical Pavement Design Guide (MEPDG) showed that local WIM traffic data inputs should be used for Alberta highways.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.994

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 communication0.0000.001
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 head0.009
GPT teacher head0.166
Teacher spread0.157 · 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