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Record W3148587533

Analyzing the energy intensity and greenhouse gas emission of Canadian oil sands crude upgrading through process modeling and simulation

2014· article· en· W3148587533 on OpenAlexaboutno aff
Alvarez-Majmutov, Jinwen, Chen

Bibliographic record

Venue化学科学与工程前沿:英文版 · 2014
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsOil sandsGreenhouse gasEnvironmental scienceIntensity (physics)Emission intensityProcess (computing)Synthetic crudeFossil fuelPetroleum engineeringWaste managementGeologyEngineeringArchaeologyUnconventional oilGeographyComputer sciencePhysics
DOInot available

Abstract

fetched live from OpenAlex

2 -intensive ( 413.2 kg 公司 2 /m 3 SCO 对 216.4 kg 公司 2 /m 3 SCO )。这实质的差别源于在基于 hydroconversion 的计划在 ebullated 床 hydroconverter 消费的大量氢,因为通过常规甲烷蒸气改过的氢生产是高度精力集中的并且因此公司的主要来源 2 排放。进一步的模拟显示操作变量的 hydroconverter 的那优化在全面公司上有仅仅次要的效果 2 排放由于在精力之间的复杂交易效果输入。

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.643

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.236
Teacher spread0.222 · 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 designSimulation or modeling
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
Published2014
Admission routes1
Has abstractyes

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