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Record W2052854905 · doi:10.1080/15459624.2013.818233

Accuracy of Reported Flash Point Values on Material Safety Data Sheets and the Impact on Product Classification

2013· article· en· W2052854905 on OpenAlexafffund
Diane Radnoff

Bibliographic record

VenueJournal of Occupational and Environmental Hygiene · 2013
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsWorkplace Health, Safety and Compensation Commission
FundersGovernment of Alberta
KeywordsFlammable liquidFlash pointProduct (mathematics)FlammabilityProduct testingHazardous wasteTest (biology)Computer scienceForensic engineeringEnvironmental scienceRisk analysis (engineering)Operations managementEngineeringBusinessWaste managementMathematicsMaterials scienceChemistry

Abstract

fetched live from OpenAlex

Material Safety Data Sheets (MSDSs) are the foundation of worker right-to-know legislation for chemical hazards. Suppliers can use product test data to determine a product's classification. Alternatively, they may use evaluation and professional judgment based on test results for the product or a product, material, or substance with similar properties. While the criteria for classifying products under the new Globally Harmonized System of Classification and Labeling of Chemicals (GHS) are different, a similar process is followed. Neither the current Workplace Hazardous Materials Information System (WHMIS) nor GHS require suppliers to test their products to classify them. In this project 83 samples of products classified as flammable or combustible, representing a variety of industry sectors and product types, were collected. Flash points were measured and compared to the reported values on the MSDSs. The classifications of the products were then compared using the WHMIS and GHS criteria. The results of the study indicated that there were significant variations between the disclosed and measured flash point values. Overall, more than one-third of the products had flash points lower than that disclosed on the MSDS. In some cases, the measured values were more than 20°C lower than the disclosed values. This could potentially result in an underestimation regarding the flammability of the product so it is important for employers to understand the limitations in the information provided on MSDSs when developing safe work procedures and training programs in the workplace. Nearly one-fifth of the products were misclassified under the WHMIS system as combustible when the measured flash point indicated that they should be classified as flammable when laboratory measurement error was taken into account. While a similar number of products were misclassified using GHS criteria, the tendency appeared to be to "over-classify" (provide a hazard class that was more conservative). So the transition to GHS may potentially decrease the possibility of "under-classifying" flammable and combustible products where no test data on the product are available.

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)0.016
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.984
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.060
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.097
GPT teacher head0.372
Teacher spread0.275 · 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.

Study designObservational
DomainReporting
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

Citations5
Published2013
Admission routes2
Has abstractyes

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