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
Monitoring pipe wall erosion/corrosion thinning rates is an important issue in petrochemical and power generation industries. In this paper, two signal processing techniques are utilized for estimating the thinning rate based on ultrasonic pipe wall thickness data collected over a short period of time. The first is a combination of cross-correlation and polynomial curve fitting and the second is a model-based estimation (MBE) scheme. These techniques are applied to data collected from an accelerated thinning rate apparatus and both show that they are capable of estimating the thinning rates quickly in short time periods with good accuracy. In laboratory applications, thinning rates as low as 10 μm/year were measured within 15 days with an uncertainty of ±1.5 μm/year by both techniques. Although the MBE technique can yield marginally better accuracy, the greater stability and computational speed of the cross-correlation technique make it the preferred choice for industrial use.
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