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Record W4236231018 · doi:10.26868/25222708.2019.210721

The Impact Of Plants On HVAC System Performance In Cold Climate: A Parametric Study

2020· article· en· W4236231018 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

VenueBuilding Simulation Conference proceedings · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsHVACCold climateParametric statisticsEnvironmental scienceComputer scienceEngineeringMeteorologyAir conditioningMechanical engineeringGeographyStatisticsMathematics

Abstract

fetched live from OpenAlex

In this paper, the TRNSYS simulation results of a building integrated agricultural space (BIAs), where a lettuce model estimated the latent and sensible heat transfers between the plants and the environment, were presented. The energy performance of two heating, ventilation and air conditioning (HVAC) systems was assessed for different cultivated densities. The results showed that the use of an economizer system reduced the total energy use of the HVAC system for high-density BIAs. It demonstrated that considering the sensible heat transfer between the plants (the cooling effect of the plants) and the environment was of crucial importance for adequate sizing of the heating equipment. The high latent heat transfer of the plants by evapotranspiration allowed adequate sizing of the dehumidification system, which is of prime importance in cold climates due to the risk of condensation in the building envelope.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.193

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 head0.038
GPT teacher head0.270
Teacher spread0.233 · 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