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Record W2337766105 · doi:10.21307/ijssis-2017-561

Industrial And Enterprise Greenhouse Gas Emission Data Analysis System

2013· article· en· W2337766105 on OpenAlex

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

VenueInternational Journal on Smart Sensing and Intelligent Systems · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutions123 Certification (Canada)
FundersProgram for New Century Excellent Talents in UniversityMinistry of Education, India
KeywordsGreenhouse gasCogenerationBoiler (water heating)Waste managementElectricityThermal power stationEngineeringExhaust gasCombustionElectricity generationProcess engineeringEnvironmental scienceEnvironmental engineeringPower (physics)Electrical engineering

Abstract

fetched live from OpenAlex

Abstract An Industrial and Enterprise Greenhouse Gas Emission Data Analysis System, which meets with the need of those giant energy-consuming enterprises, has been set up through a series of studies about Greenhouse gas (GHG) verification method in industries such as cement, thermal power, cogeneration, transportation, sewage treatment etc. The analysis system consists of six modules: Organization structure; Facility; Activity data input; Emission data analysis; Report generation; Parameters configure. The calculation process is based both on Emission Factor Method and Materials Balance Method, the former method realizes an easy calculation for the GHG emissions conducted from fuel combustion, electricity or steam consumption, general business and office activities, the later one provides a specialized calculation for professional production processes such as cement production, exhaust desulfurization and sewage treatment etc. A thermal boiler’s GHG emission was used as a case to test the system.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.745
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.033
GPT teacher head0.270
Teacher spread0.237 · 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