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Record W6959769202 · doi:10.11575/prism/49418

Techno-Economic Feasibility Study for a Deep Geothermal Systemat the Calgary International Airport

2024· other· en· W6959769202 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2024
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyGeothermal gradientGeothermal energyElectricity generationPhotovoltaic systemElectricitySolar energyInternational airport

Abstract

fetched live from OpenAlex

A techno-economic feasibility study was conducted to assess the viability of a deep geothermal system at the Calgary International Airport, given the estimated subsurface conditions and to support the Calgary Airport Authority’s net-zero targets. The National Renewable Energy Lab’s GEOPHIRES-X software was used to simulate reservoir, wellbore, and surface plant performance to estimate energy production and cost for a closed-loop geothermal system (CLGS). Results show that a CLGS is technically and economically feasible when configured for direct-use heat application. Comparison to the modelled business-as-usual forecast, demonstrates a positive impact on overall energy use and the resulting emissions and energy costs through the study period. The CLGS for direct-use heating performs better than a solar PV array with battery energy storage for the same application, however, in assessing electricity generation of the two renewable technologies, the solar PV array outperforms the deep geothermal 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.924
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.051
GPT teacher head0.306
Teacher spread0.255 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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