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Record W219337719 · doi:10.1177/0361198106196700101

Implementation of Spring Load Restrictions Using a Deflection-Calibrated Thaw Index

2006· article· en· W219337719 on OpenAlexaffabout
Stephen Goodman

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsConstellation Brands (Canada)
FundersUniversity of MinnesotaMinnesota Department of Transportation
KeywordsDeflection (physics)SignageEnvironmental scienceTransport engineeringEngineeringBusiness

Abstract

fetched live from OpenAlex

The City of Ottawa, Canada, uses a Dynaflect to monitor the change in pavement strength during the spring thaw at 11 test sites selected to represent its arterial road network. As the average deflection of the sites approaches a threshold value, load restrictions are publicized by signage and advertisements in local newspapers. The restrictions remain in effect until deflections decrease below the threshold value. Local police equipped with portable weigh scales enforce the restrictions. The goal is to allow for the shortest possible restriction period and still ensure that the road infrastructure is protected. However, the decision to implement load restrictions is traditionally made too late, because prediction of thaw progression between weekly deflection testing is difficult. The city's pavements are occasionally subjected to full-load hauling during the initial and most critical portion of the thaw period. To predict the onset and progression of the spring thaw better, the thaw index (TI) was used in addition to deflection testing. The TI (as calibrated separately by the Minnesota Department of Transportation and Manitoba Transportation and Government Services, Canada) was analyzed to determine whether it would predict the same implementation date as did deflection data. Results suggested that calibration factors provided by either agency are sufficiently accurate in the Ottawa environment but the Manitoba calibration is slightly more precise. Future investigation will involve use of surface curvature index, as well as direct temperature measurements from the city's Road Weather Information Systems.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Observationalhigh
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.230

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.140
GPT teacher head0.391
Teacher spread0.251 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2006
Admission routes2
Has abstractyes

Explore more

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