Bibliographic record
Abstract
Abstract Electroplating has, over recent decades, evolved from an art to an exact science. This development is seen as responsible for the ever‐increasing number and widening types of applications of this branch of practical science and engineering. Some of the technological areas in which means and methods of electroplating constitute an essential component are all aspects of electronics: macro and micro, optics, opto‐electronics, and sensors of most types, to name only a few. In addition a number of key industries such as the automobile industry (that uses, for example, chrome plating to enhance the corrosion resistance of metal parts) adopt the methods even where other methods, such as evaporation, sputtering, chemical vapor deposition (cvd) and the like are an option. That is so for reasons of economy and convenience. By way of illustration it should be noted that modern electroplating equips the practitioner with the ability to predesign the properties of surfaces and in the case of electroforming those of the whole part. Furthermore, the ability to deposit very thin multilayers (less than a millionth of a cm) via electroplating represents yet a new avenue of producing new materials. A brief historical discussion, the process of electroplating, preparatory (cleaning) steps required, bath formulations, operation conditions as well as the properties of deposits are presented. Recent key changes in the electroplating industry receive special emphasis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".