Validation of an offshore occupational accident frequency prediction model—A practical demonstration using case studies
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
Abstract A model has been developed to predict the frequency and associated costs of occupational accidents in the offshore oil and gas industry. Model inputs include: (i) direct factors, such as quality of personal protective equipment; (ii) corporate factors, such as training program effectiveness; and (iii) external factors, such as royalty regime. Three applications of the model are described, two for projects in eastern Canada and one for the Gulf of Mexico drilling sector. Expert opinion is used to provide the required model input associated with the regions' safety programs. Published accident data are used to calibrate the model and validate results. The model is shown to predict actual results well, especially considering the subjective nature of the activity. The model's versatility is demonstrated through its application to different types of accident statistics and regions, and its use in generating performance measures for operators. © 2006 American Institute of Chemical Engineers Process Saf Prog, 2006
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it