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Record W2329711666 · doi:10.5575/geosoc.2013.0036

Multiple-point geostatistical modeling of incised valley morphology

2013· article· en· W2329711666 on OpenAlexaff
Takashi Tsuji, Kinya Okada, Gyuhwan Jo, Arata Katoh, Koji Kashihara, Koji Kawada, Osamu Takano, Tomomi Yamada

Bibliographic record

VenueThe Journal of the Geological Society of Japan · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Geostatistics and Mapping
Canadian institutionsGeoscience BC
Fundersnot available
KeywordsGeologyGeochemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.226
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2013
Admission routes1
Has abstractyes

Explore more

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