Forecasting breaks of oil and gas pipelines
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
Even though oil and gas pipelines are the safest way to transport petroleum products, they still break generating hazardous consequences and irreparable environmental damages. Many models have been developed in the last decade to predict pipeline failure and conditions. However, most of these models were limited to one break type, such as corrosion, or relied mainly on expert opinion analysis. The objective of this paper is to develop a model that predicts the break cause of oil and gas pipelines based on factors other than corrosion. A fuzzy-based model was developed to help decision makers predict break occurrence using fuzzy expert system (FES) according to historical data of pipeline accidents. The model was able to satisfactorily predict pipeline breaks due to mechanical, operational, corrosion, third party, and natural hazards with an average percent validity of 93%. The developed model will assist decision makers and pipeline operators to predict the expected break cause(s) and to take the necessary actions to avoid them.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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