SAFE OPERATING PROCEDURE FOR FETTLING OPERATIONS AND AIR POLLUTION CONTROL IN FOUNDRY
Bibliographic record
Abstract
Abstract— The ultimate aim of an industry is to create a work place safe for their workers. One of the important ways to achieve a safe working place is a safe working procedure. The aim of my project is to observe and analyze all unsafe working procedure in all fettling operations and then prepare a Safe operating procedure in each operation in fettling area. A substance in the air that can be adverse to humans and the environment is known as an air pollutant. Pollutants can be in the form of solid particles, liquid droplets, or gases. In addition, they may be natural or man-made. Controlling and curtailing air pollution from industrial sources is essential to improving Canadian air quality. Industrial sources of air pollution include factories, electrical generation plants and incinerators. Because these sources exist in fixed locations, they are often referred to as point sources. Air pollution is the major and obvious hazard in the industry like cement industry, foundry, mines etc. controlling or eliminating air pollution is very essential has it may cause harm to the workers instantly or chronically. The aim of my project is to perform source emission monitoring & ambient air quality monitoring and compare with National Ambient Air Quality Standards and control them with proper control measures.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".