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Record W605480777

Safe in the Snow

2006· article· en· W605480777 on OpenAlexvenueno aff
Bob Persichetti

Bibliographic record

VenueBridges Conversations in Global Politics and Public Policy · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsOverlayIcingSnow removalSnowFrost (temperature)SAFERWinter stormStormEnvironmental scienceAsphaltFreezing rainBridge (graph theory)MeteorologyForensic engineeringEngineeringComputer scienceGeographyComputer securityCartography
DOInot available

Abstract

fetched live from OpenAlex

This article describes a new technology that shows promise for improving highway anti-icing operations. The new technology, called SafeLane Surface Overlay, is a patented combination of epoxy and aggregate that is installed in summer and fall. The overlay acts as a rigid sponge, storing anti-icing chemicals and automatically releasing them in winter as conditions develop for the formation of ice or snow. This helps prevent frost or ice from forming on road or bridge surfaces. The overlay keeps releasing anti-icing chemicals over multiple weather events, resulting in safer roads with less need for winter maintenance. The overlay currently is being tested at test sites in more than a dozen states. Preliminary results indicate that the overlay is successful in providing safety and mobility benefits while significantly reducing the need for chemical treatment during winter storms.

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 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 score0.336
Threshold uncertainty score0.864

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 head0.010
GPT teacher head0.238
Teacher spread0.228 · 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 teacher head, not a consensus.

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

Citations1
Published2006
Admission routes1
Has abstractyes

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