Friction transitions and connections to third bodies for a Cd coating on steel substrate
Bibliographic record
Abstract
Abstract Cd coating is used in aerospace industries from last five decades due to its sacrificial protection and lubrication properties. Although Cd coating is primarily used due to its sacrificial corrosion protection when applied on steel substrate, the added benefit of modifying the tribological behavior by acting as a lubricious layer gives it a leading-edge than other coatings. Often the measurement of friction coefficient (CoF) is reported as a value generated after full sliding cycle. This measurement of average CoF generally limits the study of local variation in CoF occurring within one sliding cycle, which can be significantly different with change in spatial position due to change in third body morphology. In this study, a linearly reciprocating sliding test is used to measure the CoF at a sampling rate of 800 Hz along the track length to generate triboscopic image with steel countersphere. The instantaneous CoF obtained with triboscopy is correlated with the wear track morphology using scanning electron microscope (SEM) and optical profilometer for variation in contact conditions. Tribological test performed in dry atmosphere shows an average CoF of 0.4 till the end of the test whereas with increase in relative humidity to 60%, the average CoF changes from 0.4 to 0.8 at the end of the test due to change in contact conditions. Soft Cd coating on low carbon steel substrate is used to study these variations in third body morphology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".