The Rough, the Smooth and the Ugly - an Overview of Anti-Slip Coatings for Structural Steel
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".