Data driven update to industry Life-Saving Rules
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The International Association of Oil & Gas Producers (IOGP) is a global forum in which member companies identify and share best practices to achieve improvements in many areas including upstream safety. The IOGP first published Life-Saving Rules in 2010, having based the choice of topics covered and actions recommended on an analysis of 1484 fatal incident reports and 1173 high potential event reports collected through IOGP’s annual Safety Performance Indicators. In 2017, IOGP launched an initiative called Project Safira with the aim of eliminating fatalities; one of the work streams within this project was to refresh, simplify and reduce the number of industry Life-Saving Rules to encourage industry standardisation. An industry team of subject matter experts, health, safety and environment (HSE) and operations professionals conducted a comprehensive analysis of the latest 10 years of fatality data and streamlined the original 18 rules down to nine, while retaining the level of applicability in fatality prevention and incorporating the latest thinking on human performance and lessons learned from member companies’ experiences in implementation of similar programs. The Life-Saving Rules are not intended to replace company HSE management systems but rather to complement existing organisational processes and procedures. The rules provide simple actions, in the form of ‘I statements’ which can provide a final barrier that individuals have control over, using their own actions to prevent fatalities. From 2008 to 2017, 376 workers lost their lives in incidents that may have been prevented by following one of the new nine IOGP Life-Saving Rules.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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 it