MétaCan
Menu
Back to cohort

Study on the Role of LEDs in Vegetable Growing in Xingtai Area

2013· article· en· W2091764419 on OpenAlexaff
Pei Ying Chen, Hong Lin Hu, Li Juan Liang, Zheng Li, Shu Xue Li

Bibliographic record

VenueAdvanced materials research · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Industry and Aquatic Biology
Canadian institutionsMcGill University
Fundersnot available
KeywordsLight-emitting diodeCryptochromeBlue lightPlant growthPhytochromeLeafy vegetablesLight pollutionIncandescent light bulbEnvironmental scienceRed lightEngineeringAgricultural engineeringOptoelectronicsHorticultureBiologyBotanyMaterials sciencePhysicsElectrical engineeringOptics

Abstract

fetched live from OpenAlex

The light emitting diodes (LEDs) whose wavelength is ideal for plant growth, flowering, fruition. The optical signal carrying frequency information and energy information, the result of the interaction of the light signal with plant phytochrome and cryptochrome proteins promote the plant growth effect, with blue light energy to promote green leafy growth; red light to help fruition and to extend flowering period. Plant supplementary light is an international leading high-precision technology product in the use of semiconductor lighting principle, dedicated to the production of flowers and vegetables, with the desired spectrum of light that is suitable for plants, with red and blue LED light irradiation, which can not only promote their growth, increase production, but also change the structure of their nutrition, reduce pests and diseases, and reduce the utilization of agricultural chemical products, improve food security, reduce environmental pollution, and improve the quality of crops in Xingtai area.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.105
GPT teacher head0.330
Teacher spread0.225 · 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 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
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueAdvanced materials researchSame topicFood Industry and Aquatic BiologyFrench-language works237,207