Multiple-point geostatistical modeling of incised valley morphology
Bibliographic record
Abstract
石油・ガスの探鉱開発では，地質モデルに地質概念を統合することが不可欠である．しかし，複雑な形状を含む地質概念の統合は，従来の確率論的モデリング手法では困難であった．最近になり，この機能に優れるとされるモデリング手法（多点法）が実用化されてきた．多点法では訓練像を介して，地質概念をモデルに統合する．しかし，複雑な形状を含むモデルの構築に，多点法はこれまで用いられていない．そこで，複雑な樹枝状を示す侵食谷地形を模したリファレンスモデルを準備し，その再現に多点法を適用し，多点法の有効性を検討した．リファレンスモデルに対応する各種データと訓練像を作成し，それらを用いて多点法を実行したところ，様々なスケール・解像度のデータが地質概念とともにモデルに統合でき，同時にリファレンスモデルに示された侵食谷形状が再現された．この結果，複雑な形状を呈する貯留層のモデリングでも多点法が有効であることが判明した．
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".