HESSD ’98 17 Safety concerns at Ontario Hydro: The need for safety management through incident analysis and safety assessment
Bibliographic record
Abstract
Safety management and the long-term operation of complex socio-technical systems Ontario Hydro-- one of the largest electrical utilities in North America-- recently decided to shut down 7 of its 20 nuclear power plants at an estimated cost of $8 billion Canadian. The motivation for this unprecedented step was not technological problems, but rather inadequate management which led to a minimally acceptable level of safety (Andognini, 1997). This paper draws examples from a recent field study conduced at Pickering NGS (Vicente, 1997) to show how system safety can decline if not scrupulously managed. These plant closures emphasize the variable nature of system safety. System safety cannot be quantified and assessed at the beginning of a system’s operation and expected to remain constant after years of operation. Many elements of a complex socio-technical system evolve with time, interacting to affect safety in unknown ways. Changes in instrumentation, number and qualifications of operators, operating conditions, and organizational structure can undermine safety. Expecting that safety remains constant may dangerously underestimate risk. Safety management Safety management involves continuous monitoring and intervention to maintain safety as the system evolves. A critical element of this process is monitoring system safety, which requires a reliable means of assessing the level of safety and identifying potential safety problems. This paper focuses on the requirements of monitoring system safety. In particular, this paper describes two complementary approaches that combine to provide an accurate measure of system safety: incident analysis and safety assessments. Control theory provides a useful framework to examine safety management (Kjellen, 1987; HESSD ’98 18 Safety goal
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".