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Record W2947405745 · doi:10.3390/horticulturae5020041

Light and Microbial Lifestyle: The Impact of Light Quality on Plant–Microbe Interactions in Horticultural Production Systems—A Review

2019· article· en· W2947405745 on OpenAlexaff
Beatrix Alsanius, Maria Karlsson, Anna Karin Rosberg, Martine Dorais, Most. Anika Naznin, Sammar Khalil, Karl-Johan Bergstrand

Bibliographic record

VenueHorticulturae · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsUniversité Laval
FundersStiftelsen LantbruksforskningVetenskapsrådetSvenska Forskningsrådet Formas
KeywordsGreenhouseAbiotic componentMicroclimateEnvironmental scienceArtificial lightBiotechnologyBiologyAbiotic stressPhototrophAgricultureEcologyAgronomyBotanyPhotosynthesis

Abstract

fetched live from OpenAlex

Horticultural greenhouse production in circumpolar regions (>60° N latitude), but also at lower latitudes, is dependent on artificial assimilation lighting to improve plant performance and the profitability of ornamental crops, and to secure production of greenhouse vegetables and berries all year round. In order to reduce energy consumption and energy costs, alternative technologies for lighting have been introduced, including light-emitting diodes (LED). This technology is also well-established within urban farming, especially plant factories. Different light technologies influence biotic and abiotic conditions in the plant environment. This review focuses on the impact of light quality on plant–microbe interactions, especially non-phototrophic organisms. Bacterial and fungal pathogens, biocontrol agents, and the phyllobiome are considered. Relevant molecular mechanisms regulating light-quality-related processes in bacteria are described and knowledge gaps are discussed with reference to ecological theories.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.276
Teacher spread0.256 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations58
Published2019
Admission routes1
Has abstractyes

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