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Record W2979652089 · doi:10.1346/cms-wls-22.3

Surface chemistry of oil-sands clay minerals

2018· book-chapter· en· W2979652089 on OpenAlexaboutno aff
Cliff T. Johnston

Bibliographic record

VenueClay Minerals Society eBooks · 2018
Typebook-chapter
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOil sandsTailingsClay mineralsGeologyAsphaltExtraction (chemistry)GeochemistryMining engineeringChemistryMetallurgyArchaeologyMaterials scienceGeography

Abstract

fetched live from OpenAlex

The oil-sand ores of northern Alberta provide a significant proportion of the overall energy portfolio for North America. Surprisingly, the presence of nano-sized clay minerals plays a defining role both in the extraction of bitumen and in tailings management. Although seemingly insignificant in size, naturally occurring clay minerals present in the oil sand ores of northern Alberta create significant challenges in all aspects of bitumen extraction and recovery, processing of oil sand ores, and management of tailings. Although a significant body of knowledge exists in relation to the characterization of ‘oil-sands clay minerals,’ much of this work has focused on the identification of the clay minerals present and not on their respective surface chemistries. This chapter focuses on some of the unique structural features of the clay minerals found in the oil sands and their respective surface chemistries.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.019
GPT teacher head0.230
Teacher spread0.211 · 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 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

Citations6
Published2018
Admission routes1
Has abstractyes

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