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Record W2617319989 · doi:10.5539/eer.v7n1p31

Argentinean Experience in Highways Led Lighting

2017· article· en· W2617319989 on OpenAlexvenueno aff
Pablo Rubén Ixtaina, Alejandro Armas, Braian Bannert, Nicolás Bufo

Bibliographic record

VenueEnergy and Environment Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicImpact of Light on Environment and Health
Canadian institutionsnot available
Fundersnot available
KeywordsRestructuringArchitectural engineeringPower consumptionLED lampProcess (computing)Work (physics)Frame (networking)Transport engineeringElectricityComputer scienceConsumption (sociology)Power (physics)TelecommunicationsBusinessEngineeringElectrical engineeringFinanceMechanical engineering

Abstract

fetched live from OpenAlex

The impact of led luminaries on road lighting has modified traditional design patterns. The technological change proposes an installation with a higher prime cost and less energy consumption. On the one hand, the price ratio between led luminary and traditional luminary is at least 3:1. On the other, the led better energetic efficiency could allow keeping proper illumination levels with less installed power. In this frame, since mid 2013, the road concessionaires of highways which constitute the Access Network to Buenos Aires city (Argentina), together with Urban Highways of the mentioned city, began a restructuring process of their lighting systems to led technology. Framed in a review of efficiency concepts and energetic classification for road lighting installations, the work presents the main results of the previous evaluation tests and of the reconverted installations, which can be considered as the first led applications at large scale on road lighting of the region.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.155
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.091
GPT teacher head0.357
Teacher spread0.266 · 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.

Study designObservational
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

Citations1
Published2017
Admission routes1
Has abstractyes

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