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Record W6903365781 · doi:10.11575/prism/36017

What Are The Proven Utility Scale Applications For Solar Steam Systems In Alberta?

2017· other· en· W6903365781 on OpenAlexaboutno aff

Bibliographic record

VenuePRISM (University of Calgary) · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSolar energyCapital costElectricityScale (ratio)Electricity generationSolar irradianceRevenueSolar power

Abstract

fetched live from OpenAlex

This paper will assess What are the proven utility scale applications for Solar Steam systems in Alberta? Although Alberta does not have adequate sunshine to generate year-round electricity through solar technology, there is suitable irradiance to economically produce high-quality steam for use in industrial processes. Solar steam systems will empower Alberta oil industry meet its environmental commitments while providing revenues that can help recoup capital investment. This study will integrate an economic analysis as an additional dimension to energy generation potential and environmental benefits of the technology. The financial modeling will reflect the baseline cost for solar fields within NREL’s System Advisor Model. The performance-enhancing and cost-reducing model of enclosing solar mirrors in a glasshouse, proven by GlassPoint, will increase the viability of solar steam applications in Alberta. Building a solar steam generation system is seen as technically feasible, with the obstacles being mainly economic and surface right allocation.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.240
Threshold uncertainty score0.483

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.001

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 head0.014
GPT teacher head0.220
Teacher spread0.205 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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