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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 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.491
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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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