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Record W7132870893

Winter Maintenance of Permeable Interlocking Concrete Pavement: Examining Opportunities to Reduce Road Salt Pollution and Improve Winter Safety

2019· dissertation· W7132870893 on OpenAlexaboutno aff
Jeffrey Thomas Marvin

Bibliographic record

VenueTSpace · 2019
Typedissertation
Language
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsSnowSubbaseInterlockingSnow removalPollutionFreezing rainRoad surfaceIcingIce formation
DOInot available

Abstract

fetched live from OpenAlex

Permeable Interlocking Concrete Pavement (PICP) rapidly drains surface water through its paver joint spaces and therefore has the potential to prevent ice from forming during winter conditions. As a result, PICP may reduce the amount of road salt needed for de-icing paved surfaces, and it may also reduce the risk of pedestrian slipping and vehicle skidding. PICP also has the potential to reduce chloride concentrations released to the environment from winter salting practices, as melted ice and snow are temporarily retained within the aggregates of its base and subbase layers. This study evaluates the performance of an outdoor PICP and asphalt test pad over two winter seasons in Vaughan, Ontario by assessing differences in surface conditions, surface friction, surface temperatures, and chloride concentrations. PICP was found to prevent melted ice and snow from refreezing, have less drastic reductions in surface temperatures at sunset, and attenuate and delay chloride concentrations.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.341
Threshold uncertainty score0.679

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.027
GPT teacher head0.274
Teacher spread0.247 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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