COMPARATIVE ANALYSIS OF MARKOV CHAIN MODEL ESTIMATION METHODS BASED ON VISUAL INSPECTION DATA
Bibliographic record
Abstract
社会基盤施設の統計的劣化予測では，目視点検データを用いてマルコフ推移確率を推定し，マルコフ連鎖モデルにより劣化過程を記述する方法論が多く提案されているが，各種推定手法の有効性や適用可能性について実データを用いた比較事例は少ない．したがって，実務においてアセットマネジメントを実践する際に劣化予測手法の選択基準が明確であるとは言い難い．また，推定手法には，実務者が自ら適用するにあたって，数学的・技術的な障壁を有する手法が存在する．本研究では，各種推定手法の特長や制約条件について既往研究をもとに整理する．その上で，実点検データを用いて，想定される条件下においてマルコフ推移確率を推定し，各種手法が有効的に機能する条件や，適用が不適切になり得る状況を分析する．
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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 | 0.021 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".