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Record W4309090957 · doi:10.3390/horticulturae8111019

Uniform Illumination Using Single-Surface Lens through Wavefront Engineering

2022· article· en· W4309090957 on OpenAlexafffund
Aria Moaven, Hamid Pahlevaninezhad, M. Pahlevaninezhad, Majid Pahlevani

Bibliographic record

VenueHorticulturae · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced optical system design
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsOpticsIlluminanceWavefrontLight-emitting diodeLight sourceGreenhouseLens (geology)OptoelectronicsPhysicsMaterials scienceEnvironmental scienceHorticultureBiology

Abstract

fetched live from OpenAlex

Recent advancements in high power light-emitting diode (LED) technology have presented greenhouse industry with a more efficient and flexible alternative light source for horticulture. However, the light distribution on the plant remains a challenge that has notable implications on the plant growth. A non-uniform light distribution on the canopy with hot spots is well-known to adversely affect the yield. Here, we present a technique to engineer the light wavefront of a solid-state source using a single-surface optics, which yields a highly uniform light distribution across the plant. This technique achieves over 90% illuminance uniformity, preserved at various distances from the source, for a cone of light with an up to 120° angular range. This work aids the paradigm shift towards LEDs as a competitive light source in horticulture.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.026
GPT teacher head0.210
Teacher spread0.184 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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