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Record W4404596744 · doi:10.2298/tsci2405435k

Exergy-rational utilization of low temperature geothermal and sewer heat in districts

2024· article· en· W4404596744 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThermal Science · 2024
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon footprintWaste heatExergyIncinerationEnvironmental scienceWaste managementGeothermal energyGeothermal gradientHeat pumpGeothermal powerEnvironmental engineeringGeothermal heatingExergy efficiencyGreenhouse gasEngineeringHeat exchangerMechanical engineeringGeology

Abstract

fetched live from OpenAlex

Aligning with the decarbonization roadmap of the EU and fifth-generation district heating systems, an exergy-based optimization and decision-making model was developed for minimum CO2 emission responsibilities. Nine environmental, thermal, and electromechanical constraints were applied. Seven cases are presented, including sewer heat in Bavaria and Toronto, Jincheon eco-friendly energy town, low enthalpy geothermal heat, a data center, waste incineration plant in Amsterdam, waste heat from the stack of a coal-fired power plant, and building-scale utilization of building wastewater. Sample calculations show that the maximum carbon footprint belongs to the sewer heat system, because of the larger temperature peaking requirement. The minimum carbon footprint belongs to the geothermal heat utilization 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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.210
Teacher spread0.204 · 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