Long Term (1970 to 2015) Trending of the Nine Prescriptive Pipeline Threats
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
Since the 1970s, the United States Department of Transportation (USDOT) Pipeline and Hazardous Materials Safety Administration (PHMSA) has collected and published pipeline failure incident data. Operators are required to report pipeline incidents and provide the apparent cause of failures. PHMSA and ASME (B31.8S for gas and B31.4 for liquids) identify and group these failures into nine broad categories and sub-classify them into three clusters by their time-based behavior. Technical advancements in pipe manufacturing, fabrication, construction, operation, inspection, monitoring, maintenance, rehabilitation and regulation have resulted in a decrease in incidents for many of these failure causes. This paper presents a statistical trending analysis of the failure incidents for each of the nine threats. The multi-year trending of these incident metrics over the last 40+ years will be demonstrated.
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) | 0.001 | 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