Sampling variance as a function of single-increment size for estimation of bitumen in an oil-sand core
Bibliographic record
Abstract
The relationship between sampling variance and single-increment size for bitumen content in oil sand was investigated for a previously collected data set of 1468 contiguous 1-cm2 IR measurements on a 15-m core. A variogram study showed that bitumen in the 15-m core is highly regionalized with a range of influence of 20 cm. For increment sizes lower than this range, the sampling variance as a function of increment size fits a logarithmic function but does not fit Visman's reciprocal function, which was also confirmed for a second oil-sand core with a range of influence of 100 cm. Visman's equation does, however, give good results for larger increment sizes. Computer randomization of the data reveals that Visman's equation is valid for either local or overall random distributions. Characterization by fractal dimension showed that Visman's equation is applicable to populations with higher fractal dimensions (equal or close to 2), whereas a logarithmic relationship is more applicable to populations with lower fractal dimensions.Key words: Sampling, geostatistics, variogram, Visman's equation, oil sand.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".