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Record W1527550940 · doi:10.1002/9781118985960.meh421

Clean Power Generation from Coal

2015· other· en· W1527550940 on OpenAlexaff
James W. Butler, Prabir Basu

Bibliographic record

VenueMechanical Engineers' Handbook · 2015
Typeother
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsClean coalCoalClean coal technologyElectricity generationUSableFossil fuelWaste managementEnvironmental scienceElectricityEnvironmental economicsProcess (computing)EngineeringEnvironmental protectionEnvironmental engineeringPower (physics)Computer science

Abstract

fetched live from OpenAlex

Abstract Coal has consistently accounted for about 40% of the world's total electricity generating capacity since the early 1970s, despite the steady growth of the total generation capacity. A number of technologies are available or under development to make the process of converting coal into a transmittable form of energy a less polluting process. These technical options can be broadly divided into the following three categories, based on which stage of the conversion process the pollutant reduction takes place: preconversion technology, In situ control technology, postconversion technologies. As emission standards become more stringent, new and cleaner ways of converting coal to usable forms of energy are being developed and implemented. Postconversion clean‐up is necessary for all types of coal‐fired plants to reduce their environmental impact. Coal produces more carbon dioxide than any other fossil fuel, and as such, the concern over global warming is a major issue with coal‐fired power plants.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.055
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0550.015

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 head0.184
Teacher spread0.177 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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