Non-Newtonian Fluids and Uncontrolled Emission of Toxic Gases: A Major Threat to Worker Safety
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".