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Record W2626686144 · doi:10.11575/prism/26279

Geochemical Characterization of the Second White Speckled Shale Formation, Western Canada Sedimentary Basin and the Mass Fraction Maturity Defining Thermal Maturity Level

2016· dissertation· en· W2626686144 on OpenAlexaboutno aff
Jingping Ma

Bibliographic record

VenuePRISM (University of Calgary) · 2016
Typedissertation
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMaturity (psychological)Oil shaleStructural basinGeologySedimentary rockGeochemistryFraction (chemistry)Mass fractionMineralogyGeomorphologyChemistryPaleontologyMaterials sciencePolitical scienceComposite material

Abstract

fetched live from OpenAlex

Source rocks with database of maturity variation from the Second White Speckled Shale Formation (SWS) were characterized geochemically by traditional source-related and maturity parameters. The changes of absolute concentrations with increasing thermal maturity was concluded. Biomarkers are initially generated and concentrated at immature to low mature stages during diagenesis, then their concentrations decrease dramatically with increasing maturity during catagenesis. n-Alkanes, especially light n-alkanes, are generated from kerogen thermal cracking and their concentrations increase continuously with thermal maturity in the whole oil generation window. Most polycyclic aromatic hydrocarbons are generated at early oil window to peak oil generation then destroyed at post mature stage. It is uncontroversial that crude oils are mixtures of different components charged from source rocks at different temperatures. Traditional thermal maturity parameters are unreliable when tracking maturities of crude oils. Combining absolute concentration profiles, mass fraction maturity model is mentioned for assessing maturity of crude oils.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.994

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 head0.005
GPT teacher head0.161
Teacher spread0.156 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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