MétaCan
Menu
Back to cohort
Record W2902369785 · doi:10.1039/9781788013352-00259

Application Status of Post-combustion CO2 Capture

2018· book-chapter· en· W2902369785 on OpenAlexaff
Deepak Pudasainee, Vinoj Kurian, Rajender Gupta

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCarbon capture and storage (timeline)CombustionEnvironmental scienceGreenhouse gasChemical looping combustionWaste managementCarbon dioxideCarbon fibersBio-energy with carbon capture and storageClimate change mitigationIntegrated gasification combined cycleClimate changeSyngasEngineeringChemistryMaterials scienceHydrogen

Abstract

fetched live from OpenAlex

Carbon dioxide (CO2) is a major anthropogenic greenhouse gas. The atmospheric concentration of CO2 has increased from 280 ppm, in the mid-1800s, to about 407 ppm in 2017. Due to the global warming and climate change effect there have been worldwide efforts to control CO2 emission. Pre-combustion capture, post-combustion capture, oxy-fuel combustion, and chemical looping combustion are the technological options currently under consideration for capturing CO2 from combustion and gasification facilities. Carbon capture and storage (CCS) has been accepted as a primary option to mitigate anthropogenic CO2 emissions. There are few large-scale CCS facilities in operation at present: (i) Petra Nova Carbon Capture, Texas, USA; (ii) SaskPower Boundary Dam- CCS; (iii) Kemper County Energy Facility (IGCC + CCS); and (iv) Callide – Oxy-fuel combustion and carbon storage demonstration plant. Furthermore, there have been some emerging small-scale PCC projects, most of which use ammonia or proprietary amines as a solvent.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.034
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0340.019

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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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 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

Citations4
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicCarbon Dioxide Capture TechnologiesFrench-language works237,207