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Record W1527550940 · doi:10.1002/9781118985960.meh421

Clean Power Generation from Coal

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.456
Threshold uncertainty score1.000

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)0.0430.004

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