Hard Coating is Because of Oppositely Worked Force-Energy Behaviors of Atoms
Bibliographic record
Abstract
Coatings of suitable materials of thickness of few atoms to several microns on certain substrate is the basic need of society and attend the regular attention of scientific community working in different domains; decorative and protective coatings, transparent and insulating coatings, coating medical implants and surgical instruments, coatings for drug delivery and security purposes, ultra-precision machine coatings, coating cutting tools, coatings for MEMS and NEMS, and so on. Different coatings develop under significant composition of certain nature atoms where their force-energy behaviors while certain transition state provide the provision for electron in outer ring of gas atom to clamp another energy knot clamped unfilled state in the outer ring of solid atom. Under certain process conditions, different nature atoms upto a certain extent oppositely-switch force-energy behaviors to the ones which possess those behaviors originally where they locate ground points at common mid-points of accommodating levels resulting into grasp binding. Because of adjusting expansion-contraction of clamped energy knots to electrons under different potential energy as per exerting orientational force of gravitation-levitation behaviors, different nature atoms develop structure at near ground surface substrate termed as hard coating, which is known since antiquity. On arresting different nature atoms under their binding at nearly oppositely-worked force-energy, non-conservative energies of ground surface are involved to engage the non-conservative forces exerting their neutral behavior viable at electron level. Different properties and characteristics of hard coatings such as hardness, adhesion, roughness, friction coefficient, resistivity and morphology-structure are emerged as per order of rescued force-energy of their structure. Here, hard coatings invent science opening to several new areas.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".