MétaCan
Menu
Back to cohort
Record W2066526732 · doi:10.1139/v02-039

Sampling variance as a function of single-increment size for estimation of bitumen in an oil-sand core

2002· article· en· W2066526732 on OpenAlexfundvenueno aff
Zhi Gao, Byron Kratochvil

Bibliographic record

VenueCanadian Journal of Chemistry · 2002
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsVariogramSampling (signal processing)StatisticsRange (aeronautics)LogarithmFractal dimensionFractalGeostatisticsCore (optical fiber)Function (biology)Variance (accounting)MineralogyMathematicsChemistryMathematical analysisSpatial variabilityPhysicsKrigingMaterials scienceOptics

Abstract

fetched live from OpenAlex

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-cm 2 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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.235
Teacher spread0.201 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2002
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of ChemistrySame topicMineral Processing and GrindingFrench-language works237,207