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Record W2882507655 · doi:10.1139/cjps-2017-0343

The value of red light at night for increasing basil yield

2018· article· en· W2882507655 on OpenAlexvenueno aff
J. S. Patel, Leora C Radetsky, Mark S. Rea

Bibliographic record

VenueCanadian Journal of Plant Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsnot available
Fundersnot available
KeywordsBasilicumOcimumSweet BasilDowny mildewHorticulturePhotosynthesisRed lightYield (engineering)BotanyBiology

Abstract

fetched live from OpenAlex

Sweet basil (Ocimum basilicum L.) is primarily used for culinary purposes, but it is also used in the fragrance and medicinal industries. In the last few years, global sweet basil production has been significantly impacted by downy mildew caused by Peronospora belbahrii Thines. Nighttime exposure to red light has been shown to inhibit sporulation of P. belbahrii. The objective of this study was to determine if nighttime exposure to red light from light-emitting diodes (λ max = 625 nm) could increase plant growth (plant height and leaf size) and yield (number and weight of leaves) in basil plants. In two sets of greenhouse experiments, red light was applied at a photosynthetic photon flux density of 60 μmol m −2 s −1 during the otherwise dark night for 10 h (from 2000 to 0600). The results demonstrate that exposure to red light at night can increase the number of basil leaves per plant, plant height, leaf size (length and width), and leaf fresh and dry weight compared with plants in darkness at night. The addition of incremental red light at night has the potential to be cost-effective for fresh organic basil production in controlled environments.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.561
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.204
Teacher spread0.186 · 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.

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

Citations5
Published2018
Admission routes1
Has abstractyes

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