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Record W2134029260 · doi:10.5539/eer.v3n2p33

Assessment of Investment in Small Hydropower Plants

2013· article· en· W2134029260 on OpenAlexvenueno aff
Perica Ilak, Slavko Krajcar

Bibliographic record

VenueEnergy and Environment Research · 2013
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsHydropowerInvestment (military)Flow (mathematics)Work (physics)Value (mathematics)Linear programmingComputer scienceEnvironmental scienceHydrology (agriculture)Water resource managementEnvironmental economicsMathematical optimizationMathematicsEconomicsGeologyEngineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

In this study an assessment of investments in a hydropower plants is considered. The objective of this work is to maximize the net present value from selling energy. Because of the stochastic nature of river flows, flow duration curve is constructed to take into account hydropower plant capacity factor, important for the assessment of investment. Proposed mixed-integer linear programming model is flexible and accounts for nonlinear three-dimensional (3-D) relationship between the produced power, the discharged water, and the head of the associated reservoir. Continuous chain of imaginary run-of-river plants is considered on the Sava river stretch (Croatia) from border with Slovenia to city of Sisak.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.350

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 head0.021
GPT teacher head0.234
Teacher spread0.212 · 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 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

Citations2
Published2013
Admission routes1
Has abstractyes

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