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Record W4389435229 · doi:10.3390/plants12244099

Shade Effect on Phenology, Fruit Yield, and Phenolic Content of Two Wild Blueberry Species in Northwestern Ontario, Canada

2023· article· en· W4389435229 on OpenAlexafffundabout
Viktoriya Dyukaryeva, Azim U. Mallik

Bibliographic record

VenuePlants · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsLakehead University
FundersLakehead University
KeywordsBerryPhenologyVacciniumBiologyYield (engineering)HorticultureBotanyAntioxidant capacityVegetative reproductionBotanical gardenAntioxidant

Abstract

fetched live from OpenAlex

We studied the effect of shade on the phenology, growth, berry yield, and chemical content of two common blueberry species (Vaccinium myrtilloides and V. angustifolium) in Northwestern Ontario. We hypothesized that high shade would delay vegetative and reproductive phenology and decrease berry yield by increasing resource allocation to vegetative vs. reproductive growth, whereas moderate shade would increase berry phenolic content and antioxidant capacity. We subjected transplanted blueberry plants to a controlled shade treatment and evaluated plant phenological events, vegetative and reproductive growth, berry phenolics, and antioxidant capacity. High shade caused an earlier leaf maturation in V. myrtilloides, delayed flowering in V. angustifolium, and prolonged fruit maturation in both. The berry yield of both species decreased with increasing shade. High shade reduced the berry phenolic content and antioxidant capacity, especially in V. myrtilloides. We concluded that shade shifts species-specific vegetative and reproductive phenology, leading to a difference in resource acquisition, resulting in lower berry yield and antioxidant activity.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.076
GPT teacher head0.246
Teacher spread0.171 · 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 designObservational
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

Citations4
Published2023
Admission routes3
Has abstractyes

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