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Record W4382790098 · doi:10.4236/ojsst.2023.132004

Non-Newtonian Fluids and Uncontrolled Emission of Toxic Gases: A Major Threat to Worker Safety

2023· article· en· W4382790098 on OpenAlexaff
Neil McManus

Bibliographic record

VenueOpen Journal of Safety Science and Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsHealth Sciences North
Fundersnot available
KeywordsEnvironmental scienceHazardous wasteToxic gasCarbon dioxideHydrogen sulfideWaste managementMaterials scienceEnvironmental engineeringChemistryEngineeringSulfur

Abstract

fetched live from OpenAlex

This article explores the role of shear-thinning, pseudoplastic non-Newtonian fluids in the causation of fatal accidents involving hazardous atmospheres. Analysis of fatal accidents indicates an appreciable likelihood of occurrence in structures in the infrastructure. When subjected to a shear force, such as stirring, stored gas emits from these fluids. Depending on the gas, lethal concentrations can develop almost instantaneously. Upon cessation of the stress, the ambient condition restores rapidly. Chemical and physical processes that provide reservoirs for the storage of gases potentiate anaerobic respiration by microorganisms as the source of the gases stored in some non-Newtonian fluids. Hydrogen sulfide (H2S) is the most toxic gas followed by carbon dioxide (CO2) and ammonia (NH3). Very limited methods are available for controlling exposure to gases emitted during the disturbance of these fluids. Control strategies must accommodate almost instantaneous high-level emissions. Disturbance under controlled conditions that isolates emissions from workers is a possible means to minimize the risk of exposure. Engineering design that incorporates suitable control devices into equipment and structures is critical to the success of this strategy. The Prevention through Design program created by the U.S. National Institute for Occupational Safety and Health is a formal initiative for coordinating an organized response to these challenges.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.008
GPT teacher head0.260
Teacher spread0.251 · 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 designBench or experimental
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

Citations4
Published2023
Admission routes1
Has abstractyes

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