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Record W4412124023 · doi:10.13031/aim.202500157

Statistical Analysis of the Impact of Weather Parameters on Wild Blueberry Yield (A Case Study; Nova Scotia)

2025· article· en· W4412124023 on OpenAlexaboutno aff
Mona Golabi, Travis J. Esau

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaNova (rocket)Yield (engineering)Statistical analysisEnvironmental scienceMeteorologyStatisticsComputer scienceGeographyMathematicsAeronauticsEngineeringPhysicsArchaeology

Abstract

fetched live from OpenAlex

Abstract. Wild blueberries (Vaccinium angustifolium) are an important agricultural and ecological resource in Eastern Canada. Known for their economic value and contribution to biodiversity, wild blueberries thrive in regions with cold winters, essential for dormancy and bud development, and moderate summer temperatures between 20–27°C. They require acidic, well-drained sandy or loamy soils with a pH of 4.2–5.2, enriched with organic matter to retain moisture and provide nutrients. However, the impacts of climate change pose significant challenges to their growth and sustainability. This paper explores the effects of climate parameters on the yield of wild blueberries in Nova Scotia. The data from 1996-2016, the Pearson correlation coefficient, and the principal component analysis (PCA) have been used to evaluate the effects of climate parameters on the yield of wild blueberries. This paper determined the percentage impact of weather parameters on wild blueberry yield. The results indicated that temperature-related variables, particularly days with valid maximum temperature, days with valid mean temperature, and mean temperature, have the most significant impact on blueberry yield, with high temperatures negatively affecting crop productivity. Moderate precipitation levels positively correlated with yield, although excessive rainfall tended to have a detrimental effect. Factor analysis revealed that temperature-related variables accounted for more yield variability than precipitation factors. Despite some weak and marginally significant correlations, the study emphasizes the complex nature of climate-yield interactions. It suggests that other environmental factors may also play a role in determining yield. The findings contribute to a deeper understanding of the climatic influences on wild blueberry production. They can inform crop management and predictive models in the context of crop and climate change.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.523
Threshold uncertainty score0.949

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
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 head0.067
GPT teacher head0.348
Teacher spread0.281 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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