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Record W4409554692 · doi:10.5006/s2002-00017

The Rough, the Smooth and the Ugly - an Overview of Anti-Slip Coatings for Structural Steel

2002· article· en· W4409554692 on OpenAlexaboutno aff
Mike O’Donoghue, Ron Garrett, J.C.P. Garrett, Vijay Datta

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceSlip (aerodynamics)MetallurgyComposite materialEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Abstract Since their development in the early 90s to help prevent slip and fall accidents and avert workers compensation claims, anti-slip coating systems have been successfully applied in Western Canada on up to 25,000 tons of structural steel. Polyurethane finish coats that contain judiciously selected polyolefin bead media have been found to greatly improve the footing for ironworkers during erection of structural steel and also improve the loading and unloading of steel beams. This paper describes the process in solving the ironworkers safety concerns with anti-slip coating systems for structural steel. Aspects of slipping have been outlined as well as current SSPC-AISC task force activity in testing for slip resistance. The results of the author's studies to measure slip indices of 20 coating systems (with four different types of generic finishes) have been discussed. The results are important in light of the fact that for structural steel on new construction, over the next 5 years OSHA will phase-in the requirement for anti-slip coatings to have a slip index value of 0.5 English slip units (ESU). Case histories exemplify the success of anti-slip coating systems in new construction projects in the pulp and paper industry.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.136

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 head0.042
GPT teacher head0.254
Teacher spread0.212 · 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 designTheoretical or conceptual
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
Published2002
Admission routes1
Has abstractyes

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