The drive and passion of Faisal Khan to improve onshore and offshore process industries' safety levels
Bibliographic record
Abstract
Abstract In 1997, a number of articles were co‐authored and published in leading process safety journals (e.g., Journal of Loss Prevention in the Process Industries, Process Safety Progress, and Process Safety and Environmental Protection) by a young and enthusiastic scientist, Faisal Khan—motivated by a drive to improve the safety practices of the time after the 1984 Bhopal Disaster—and a well‐known Professor and Scientist from the Centre for Pollution Control and Environmental Engineering at Pondicherry University, Dr. Shahid Abbas Abbasi. It began a new horizon in the process safety domain, as this young scientist continued to excel in this field and shed light with numerous inventions and by training several hundreds of next‐generation safety professionals as a Professor at Memorial University of Newfoundland, University of Tasmania, and Texas A&M University. This article is written in honour of Dr. Faisal Khan. Capturing the fullness of Dr. Khan briefly is an arduous task; however, the authors of the current article have attempted to describe his personality, background, and contributions based on their professional interactions with him.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".