Determining the Thickness Coating of Grinding Powders of Synthetic Diamond Based on a Specific-Surface Approach and using an Extrapolation-Affine 3D Model of Grain
Bibliographic record
Abstract
: Methodological features of indirect determining of thickness coating of grains grinding powders of synthetic diamond are analyzed. A newly revised classification of known methods for determining the thickness of the coating is proposed. The prospects of the methods based on the application of an external specific surface are noted. A positive feature is the proposal to determine the thickness of the coating separately for each grain of the sample, followed by the generalization of the results by calculating the arithmetic mean. This calculation scheme allows you to get more reliable information about the thickness of the coating. The expediency of using an extrapolation-affine 3D grain model in such a calculation scheme is substantiated. Using the extrapolation-affininе 3D grain model allows for determining the thickness of the coating of diamond powder grains without the traditional assumption about the spherical shape of their grains and with less error. For an example of grinding powder AC125 400/315, the advantage of such a 3D model compared to a 3D model in the form of a sphere is proved. The method proposed on the basis of such methodical innovation can be used for powders of other abrasive materials.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".