On friction measurements and certification of slip‐preventing materials for road freight usages
Bibliographic record
Abstract
Abstract Slip‐preventing materials are often used as tertiary packaging to improve cargo securement and simplify truck loading. There are many national and international standards on their safe usage based on the extra friction they can provide. However, it is challenging to quantify the coefficient of friction (CoF) of slip‐preventing materials. This paper presents CoFs of three slip‐preventing materials and one baseline condition measured on different surfaces and contact pressures. These measurements show interesting phenomena that affect the result variability. For instance, 60% of the pairs of surfaces tested were affected by the contact pressure. Hence, the load of the freight seems to affect performance of the slip‐preventing materials. This interaction happened in different ways such as changes in the friction mode (constant slip and stick slip at difference frequencies and magnitudes). The typical CoF values available in standards as well as standardized measurement methods do not consider very well these effects nor how to deal with high variability of CoF measurements. This can lead to either over or under CoF estimations. To overcome these limitations, this paper presents a statistical method to quantify slip‐preventing material CoFs based on variability and confidence intervals of the measurements.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".