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Record W564682364

DEEP REHAB FOR DISTRESSED PAVEMENTS

2002· article· en· W564682364 on OpenAlexaboutno aff
K Landers

Bibliographic record

VenueBetter roads · 2002
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsTerrainOverlayForensic engineeringTask (project management)Mixing (physics)EngineeringCivil engineeringSound (geography)Transport engineeringMining engineeringGeologyComputer scienceGeographyCartography
DOInot available

Abstract

fetched live from OpenAlex

Cold-in-place recycling (CIR) is useful for older roads with sound structure but pavement that is too deeply distressed, cracks two inches deep or more, for surface repairs such as hot-in-place recycling or overlays. Separate machines are used for each task in a train, for milling; screening and crushing; and mixing. Most productive in open terrain and can be done rapidly, as much as two lane-miles in a day, and compact, from 1.25 to 1.5 lanes for equipment, reducing detour needs. The resulting pavement is more flexible, which makes it good for regions with extreme changes in temperature. Heavier rollers are required for the more viscous mix. New York's Fulton County and Ontario's County of Wellington are two experienced users reporting satisfactory 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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.681

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)0.0010.000

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 head0.022
GPT teacher head0.227
Teacher spread0.205 · 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 designSimulation or modeling
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

Citations2
Published2002
Admission routes1
Has abstractyes

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