A Decision-Making Expert System for the Oil Transport System
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 The problem of pipeline corrosion within the oil and gas industry costs the world economy billions of dollars every year in maintenance, repairs and too often in damage control. These costs are passed on, reflected in increased prices to the world's petroleum product consumers. With the advent of widely available computing and communications technology, it is logical that we should seek relief from corrosion and maintenance problems in the form of a high-tech solution. To this end the authors have developed an expert system, the Petroleum Corrosion and Coating Expert System (PCCES) equipped with an extensive knowledgebase of physical and chemical phenomena and the metallurgical characteristics of the pipes themselves. Essentially a complex decision tree, the expert considers factors in a real-world situation and attempts to produce appropriate conclusions based on inference from the knowledgebase. For greater ease of use, the expert system relies on a Java applet design, eliminating the need for proprietary client-side software.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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