Methodology for the Evaluation of Cleaning Pigs on Sludge Deposits from Corrosion Pits
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 For decades pipelines have been operated in remote and environmentally sensitive areas as well as within populated locations. Proper maintenance of pipelines can prevent internal corrosion to a remarkable degree. The methods employed are primarily mechanical cleaning (pigging) and chemical treatment (corrosion inhibitors, biocides), often used in combination. Corrosion issues arise in areas of the pipeline typically under localized areas containing sediments that tend to be an agglomeration of solids, waxes and water. The resulting corrosion defects can then become ideal locations for sediment and water to continue to gather and create deep, dirt filled localized pitting that cannot be protected through chemical treatment without the aid of mechanical cleaning (pigging). In an effort to increase the knowledge of the cleaning efficiency of typical pig designs at removing sludge and debris from pre-existing corrosion pits, a novel test setup and method has been devised. A recirculating flow loop was constructed with the capabilities of launching a 102 mm (4”) diameter cleaning pig using either crude oil or water as the pumped fluid. During the test, a pig would be passed through a test apparatus which housed flush mounted coupons with variously sized pits, packed with manufactured sediment (sludge). Following the pigging operation, the coupons were removed and analyzed via laser scanning techniques to measure sludge volume removal and maximum depth of cleaning. The pigs’ cleaning abilities were compared based on both metrics and information was gathered based on the profile of the sludge’s surfaces post pigging, as well as images of the pigs with adhered sludge.
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 | 0.000 |
| 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