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
Diamond-like carbon (DLC) coatings are recognized in many sectors as a promising way of controlling wear and the corrosion performance of components. DLC coatings are well established in the automotive industry where they are applied to the moving parts of direct injection fuel systems operating under frictional conditions at high pressures and in the aggressive environment of the combustion chamber. Over the last few years, there has also been an increasing number of reports of DLC coating applications in oil and gas production contexts, including in pipes, shut-off gates and various types of valves. This paper reviews current efforts to use DLC coatings in the oil and gas sectors and analyses typical coating degradation mechanisms including wear and wear-accelerated corrosion regimes. DLC coating deposition techniques, including Physical (PVD) and Chemical Vapor Deposition (CVD) techniques, are elaborated, and the unique coating properties obtained from those two methods are assessed. Surface functionalization is discussed, including dopants (W and Si) and gradient interlayers. Finally, the outlook for future use of DLC coatings in oil and gas production is discussed.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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