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Dual use solar surfaces for local grids and net zero development

2020· article· en· W3127719447 on OpenAlexaff
Andrew Csinger, Douglas Matthews

Bibliographic record

Venue2020 IEEE Electric Power and Energy Conference (EPEC) · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicPhotovoltaic Systems and Sustainability
Canadian institutionsMorgan Solar (Canada)
Fundersnot available
KeywordsPhotovoltaicsSoftware deploymentMisappropriationSolar powerSolar energyEnvironmental scienceZero-energy buildingReal estateEnvironmental economicsArchitectural engineeringComputer scienceTelecommunicationsElectrical engineeringEngineeringBusinessPhotovoltaic systemPower (physics)EconomicsPhysicsFinance

Abstract

fetched live from OpenAlex

Legacy approaches to energy management and distribution are ill suited to solar power. The history of hydro, nuclear and coal offer little to guide contemporary photovoltaics infrastructure deployment. Putting gigawatts of solar panels on the cheapest available real estate will have lasting negative consequences: new approaches are needed to avoid unnecessary, undesirable and both predictable and unpredictable environmental, social and economic side effects.Rather than taking over productive farmers' fields with gigawatt scale solar plantations that unbalance grids and introduce new transmission needs, the many sunny surfaces proximal to load should be pressed into dual-use: efficient pavement integrated photovoltaics (PIPV) avoid misappropriation of real-estate, do not affect current use or aesthetics of surfaces and require no transmission lines. We call this dual-use of high value real estate surface solar, and demonstrate the opportunity for superior IRR over traditional deployment.

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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.201
Teacher spread0.187 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2020
Admission routes1
Has abstractyes

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