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Record W4409201984 · doi:10.1080/19315260.2025.2487018

Effect of amber and low-pressure sodium lights supplemented with blue light on lettuce ( <i>Lactuca sativa</i> ) production

2025· article· en· W4409201984 on OpenAlexafffund
Felix Marcil-Gendreau, Philip Addo Wiredu, Anne-Sophie Rufyikiri, Sarah MacPherson, Mark Lefsrud

Bibliographic record

VenueInternational Journal of Vegetable Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaUrban Barns Foods
KeywordsLactucaBlue lightProduction (economics)Sodium-vapor lampEnvironmental scienceSodiumHorticultureBiologyPhysicsEconomicsChemistryOptics

Abstract

fetched live from OpenAlex

As food security concerns and controlled environment agriculture continue to grow, a comprehensive understanding of the influence of light on vegetable cultivation is needed. This study compared the performance of broad amber (595 nm) lamps to low-pressure sodium lamps (589 nm) and evaluated whether the addition of blue (450 and 473 nm) light improved plant growth. The low-pressure sodium lamp performed similarly to amber in terms of vegetative growth. Results showed that lettuce grown under low-pressure sodium lamps exhibited yellowing of leaves, which was not observed under amber light. The addition of blue light significantly (p < .05) reduced the fresh mass and dry mass of plants treated with low-pressure sodium lamps by 21.5% and 16.6%, respectively. For plants treated with amber light, the addition of blue light at light intensities below 170 μmol m−2 s−1 significantly (p < .05) increased both fresh mass and dry mass, although decreased by 30.4% and 19.5%, respectively, above 170 μmol m−2 s−1. Data presented herein show that the addition of blue LEDs significantly increased the chlorophyll concentration by 24.4% and 33.1% for low-pressure sodium lamps and amber light, respectively (p < .05).

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 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 score0.017
Threshold uncertainty score0.183

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 communication0.0000.001
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 head0.003
GPT teacher head0.229
Teacher spread0.226 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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