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Record W7117952313

Risk analysis of firm energy coverage in colombia in the medium term

2021· article· en· W7117952313 on OpenAlexaboutno aff
Ricardo Moreno-Chuquen, Sergio Alejandro Cantillo Luna, Lilian Andrea Carrillo Rodríguez

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2021
Typearticle
Languageen
FieldPsychology
TopicEducational methodologies and cognitive development
Canadian institutionsnot available
Fundersnot available
KeywordsTerm (time)Order (exchange)ElectricityPosition (finance)Energy (signal processing)Medium termQuarter (Canadian coin)Energy supplyCarry (investment)
DOInot available

Abstract

fetched live from OpenAlex

The recent auction of rm energy and the decisions on medium-term coverage give rise to risks in the supply of electricity in Colombia in the coming periods. Taking into account the possible risks that may arise, such as: non-compliance with FEO due to generation units (six [6] non-compliances during 2015-2016 term), the delay of generation projects with committed rm energy (Hidroituango case) and the availability of rm energy in the market, imply a systemic risk for the electric power supply in the medium term. Through the study of technical documents and resolutions, issued by the CREG, about the medium term energy balances in 2018, rm energy supply and demand balances were reconstructed, including the results of the last FEO auction carried out in the rst quarter of 2019, in order to carry out a risk analysis based on these same scenarios. It was observed that the amount of FEO auctioned exceeds the quantity of demand projected, meaning that the CREG assumed a conservative position by purchasing more energy than necessary (8650 GWh-year and 1027 GWh-year respectively), this is a situation that has occurred on more than one occasion

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0030.001
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 head0.039
GPT teacher head0.306
Teacher spread0.267 · 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
Published2021
Admission routes1
Has abstractyes

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