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Record W2327732919 · doi:10.1177/0143624414527993

Heating plant input–output efficiency in two cold-climate institutional buildings with condensing hot water boilers

2014· article· en· W2327732919 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBuilding Services Engineering Research and Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversity of Calgary
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
KeywordsBoiler (water heating)Efficient energy useEnvironmental scienceProcess engineeringEngineeringWaste management

Abstract

fetched live from OpenAlex

Seasonal input–output efficiencies of two heating plants with condensing boilers in cold climate institutional buildings (3300 m 2 school and 12,000 m 2 university building) were evaluated. Plant efficiency remained about the same or declined with load, contrary to typical lab boiler ratings, which show efficiency increasing at lower loads. These results occurred when the boilers were operated with return water temperatures largely in the condensing range. Heating plant load was often 25% or less of the rated load of a single boiler, resulting in heating plant input–output efficiencies well below rated boiler efficiencies. Practical application: In-situ boiler plant input–output efficiencies can differ widely from manufacturer's boiler efficiency curves. For applications such as simulation for energy-efficient design, effective decision-making depends on accurately estimating real-world performance.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.229
Teacher spread0.221 · 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