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Record W6959426613 · doi:10.11575/prism/36027

Technical And Economic Feasibility Of Building A Small Hydro Plant In Two Different Sites In Alberta

2018· other· en· W6959426613 on OpenAlexaboutno aff

Bibliographic record

VenuePRISM (University of Calgary) · 2018
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogens and Resistance
Canadian institutionsnot available
Fundersnot available
KeywordsSmall hydroHydropowerEconomic feasibilityHydroelectricityCost of electricity by sourceEconomic evaluationCost–benefit analysisElectricity generationHydro powerWildlife

Abstract

fetched live from OpenAlex

Hydropower has long been used due to the ease of producing energy via water. However, construction of large dams has negative environmental impacts on wildlife as well as aquatic ecosystems, due to large land requirements to support their development. Small dams present an alternative to limit these known issues, with potential growth of around 77% in Canada alone. This research aims to assess the technical and economic feasibility of building a small hydro facility at two sites, in a pre-determined region of Alberta. Using a simplified Levelized Cost of Energy model for each site, an evaluation will determine which is more ideal to be developed first. Additionally, the environmental benefits of a large hydropower plant compared to a small hydropower plant will be discussed. The results are expected to provide the company currently investigating development at these sites with greater insight on how to proceed with project development plans.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.657

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
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 head0.016
GPT teacher head0.196
Teacher spread0.181 · 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
Published2018
Admission routes1
Has abstractyes

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