A Study on Component Reassembly Assist Method Using Simple Marker by Recording and Replaying Disassembly Order
Bibliographic record
Abstract
部品のメンテナンスや調査のために，すでに組み立てられている立体物を分解した後に再組立することがある．一般に，すでに組み立てられているものを再組立するときには，製品に付属している組立図を用いるが，組立図は二次元的な情報表現であるため，部品の細かい特徴を描写できず，表示手法に限界がある．また，組立図を紛失している場合や元々存在しない場合，組立手順を再現することが困難になる．そこで，本論文では，分解順序の記録と再生により部品の再組立を支援する手法を提案する．本手法は，ひとつの部品を分解するごとに，分解手順を記録し，再組立を行うときに適切な順序で再生することによって組立手順を再現し，ユーザに提示する手法である．
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".