Improved geostatistical models of inclined heterolithic strata for McMurray Formation, Alberta, Canada
Bibliographic record
Abstract
Abstract The McMurray Formation of northern Alberta in Canada contains multiscale complex geologic features that were partially formed in a fluvial-estuarine depositional environment. The inclined heterolithic strata deposited as part of fluvial point bars contain continuous centimeter-scale features that are important for flow characterization of steam-assisted gravity drainage processes. These channels are common, extensive, and imbricated over many square kilometers. Modeling the detailed facies in such depositional systems requires a methodology that reflects heterogeneity over many scales. This article presents an object-based facies modeling technique that (1) reproduces the geometry of multiscale geologic architectural elements seen in the McMurray Formation outcrops and (2) provides a grid-free framework that models these geologic objects without relating them to a grid system. The grid-free object-based modeling can be applied to any depositional environment and allows for the complete preservation of architectural information for consistent application to any gridding scheme, local grid refinements, downscaling, upscaling, drape surface, locally variable azimuths, property trend modeling, and flexible model interaction and manipulation. Features millimeters thick or kilometers in extent are represented very efficiently in the same model.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".