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Design Approaches to Improve Organic Solar Cells

2014· article· en· W1967420285 on OpenAlexvenueno aff
Fahmi Fariq Muhammad

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2014
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceComputer scienceProcess engineeringEngineering

Abstract

fetched live from OpenAlex

Las inversiones con criterios ESG, han tomado especial relevancia en el mundo actual de los negocios, la reciente aparición de estándares y marcos de reportes de sostenibilidad, han llevado a que, en distintas jurisdicciones, se reglamente la adopción de estas normativas en las empresas para medir su desempeño ESG y lograr proyectar a los distintos usuarios de la información su sostenibilidad al mediano y largo plazo, es por ello que la investigacion busca explora las principales normativas expedidas por el gobierno nacional, en pro de que las organizaciones privadas implanten y revelen asuntos de sostenibilidad, mediante el uso de los distintos marcos y estándares desarrollados internacionalmente. Para lograr el objetivo la metodología escogida se basó en un enfoque un cualitativo, con un diseño exploratorio , los resultados permiten determinar que en Colombia los entes de control gubernamentales han expedido normativas, que imparten instrucción, sobre la implantación de procesos ESG, el camino recorrido por entidades como la Superfinanciera, es reconocido por su solides en la región, al ser referente en temas de inversiones sostenibles siendo la taxonomía verde su buque insignia, otro aporte es la reglamentación de las cinco dimensiones de las sociedades BIC y por último la gran apuesta en 2025 de la SuperSociedades por masificar los reportes ESG principalmente en las entidades vigiladas.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.190
Teacher spread0.169 · 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 designBench or experimental
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

Citations12
Published2014
Admission routes1
Has abstractyes

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