Ranking of metallic and non-metallic coatings in the electrochemical surface treatment sector
Bibliographic record
Abstract
The distribution of coatings by the frequency of their application during surface treatment by electrochemical methods is considered. This is important not only for understanding the structure of the electrochemical surface treatment sector, but also for identifying priority areas of scientific and technical research. Nonparametric statistical methods show the uniformity of samples and reveal the relationship between the number of enterprises that sell a certain type of coating, i.e. the frequency of applying a certain type of coating in different countries (USA, Japan, Italy, France, Germany, Great Britain, Spain, Canada, Mexico, Russia, South Africa). The results of testing the hypothesis of a close relationship between the ranks of coatings showed that a significant correlation was found between the distribution of coatings by the frequency of their application (implementation) among all countries. For example, when comparing the United States and Canada, the rank correlation coefficient is 0.62 (the lowest value obtained), which is greater than the calculated critical value of 0.56; when comparing Italy and Spain, the correlation coefficient takes the highest value of 0.97, which is greater than the critical value of 0.19. The results obtained allowed us to use this data to compile a generalized rating of the frequency of use of all coatings based on data from different countries. Based on the analysis, metal coatings can be arranged in a row according to the descending frequency of their application: Cr > Ni > Zn > Cu > Cd. The results of the ranking of coatings showed that the most commonly used electrochemical methods for surface treatment are metal coatings with chromium and nickel, and among the inorganic non – metallic coatings-oxide and then phosphate, which allows us to highlight the research devoted to the application of these coatings as priority areas of scientific and technical research.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".