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
Hurricanes are multi-hazard natural hazards that can cause severe damage to chemical and process plants via individual or combined impact of strong winds, torrential rainfall, floods, and hitting waves especially in coastal areas. To assess and manage the vulnerability of process plants, failure modes and respective failure probabilities both before and after implementing safety measures should be assessed. However, due to the uncertainties arising from interdependent failure modes and lack of accurate and sufficient historical data, most conventional quantitative risk assessment techniques deliver inaccurate results, which in turn lead to inaccurate risk assessment and thus ineffective or non-cost-effective risk management strategies. Bayesian network (BN) is a probabilistic technique for reasoning under uncertainty with a variety of applications is system safety, reliability engineering, and risk assessment. In this chapter, applications of BN to vulnerability assessment and management of process vessels in the event of hurricanes are demonstrated and discussed.
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 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