“A geological-prior-guided attention network improved tight-sandstone permeability prediction and reservoir-type classification in a 12-well case study in China's Ordos Basin.”

Checks out so far. 2 hold up so far.

0.8353 under ledger-default@0.3.0 at snapshot 27745 for all claims together. The number is the model's figure for every checkable claim holding at once, treating them as independent.

The statement as a whole, read from the 2 claims below. Each claim is scored on its own; this reading only counts them. Provisional until audited.

Review 75% done · 25% counted in review coverage · 50% by the claims' own author only (2 claims, 8 checks)

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Checked in Galedra: score 0.84, mostly supported · not yet reviewed by anyone else “A geological-prior-guided attention network improved tight-sandstone permeability prediction and reservoir-type classification in a 12-well case study in China's Ordos Basin.” https://galedra.org/investigations/01a0eab2-491c-7346-84fa-3dd281e232ab

“In a 12-cored-well case study from the Chang 8 Member of China's Jiyuan Oilfield, an SE-ResNet18 well-log model achieved R² 0.86 and RMSE 0.287 mD for tight-sandstone permeability regression.”

Supported · 0.8581 under ledger-default@0.3.0 at snapshot 27745 · review checks 1 of 4 · provisional until audited.

3 of 4 review checks done · 1 counted · 2 by the author only

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“Embedding a well-log-based sweet-spot index as a geological prior in an attention-gated KG-SE-ResNet18 model improved reservoir-type classification accuracy from 86.95% to 89.28% in the Jiyuan Oilfield case study.”

Supported · 0.9734 under ledger-default@0.3.0 at snapshot 27745 · review checks 1 of 4 · provisional until audited.

3 of 4 review checks done · 1 counted · 2 by the author only

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