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Record W3196343594

Exploring the Feasibility and Costs and Benefits of Solar Carports for the Calgary Parking Authority

2018· article· en· W3196343594 on OpenAlexaboutno aff
Jessica Polivchuk

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2018
Typearticle
Languageen
FieldEngineering
TopicSmart Parking Systems Research
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessAdvertisingEnvironmental planningGeography
DOInot available

Abstract

fetched live from OpenAlex

Solar carports – structures that shade parked vehicles, protect vehicles from the elements, and generate renewable electricity using solar photovoltaic (PV) panels – present a unique and interesting opportunity for existing surface parking lots to be transformed into multi-use spaces that advance sustainability goals and generate added financial benefits for parking providers. By generating renewable energy, solar carports provide a means for public parking providers to offset energy costs, generate revenues by selling energy to the grid, and play a role in reducing greenhouse gas emissions in alignment with local, provincial, federal, and international commitments to address and mitigate climate change. While solar carports are expensive and may be expected to result in net costs based on current cost estimates and electricity price forecasts, they present an opportunity to contribute to local public policy objectives to reduce carbon emissions and are well aligned with local policies, plans and strategies.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.640
Threshold uncertainty score0.716

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0050.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.001

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 head0.094
GPT teacher head0.284
Teacher spread0.189 · 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 designObservational
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

Citations3
Published2018
Admission routes1
Has abstractyes

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