Bibliographic record
Abstract
最近の自動車塗装技術の動向について，塗装機，塗装ロボットと制御システムについて述べた。今日の塗装技術に対してさらなる環境負荷の低減が求められており，塗装ブース面積の縮小化と多色化に柔軟に対応する塗装システムが求められている。塗装機においては，エアモータの高性能化による塗料の大吐出量化，塗料のオーバースプレー量を大幅に削減できるパターン制御技術，塗装ブース面積の縮小化と多色化を実現できる洗浄機能付カートリッジ，および塗装機に飛散した塗料粒子が付着することを防ぐ汚れ防止機構について述べた。塗装ロボットにおいては，可動範囲の広い壁掛け式ロボットと，本ロボットが可能にするブースの省面積化について述べた。制御システムにおいては，一般的な機器構成，制御方式および機器のもつ遅れの補正について述べた。
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".