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Record W2030028258 · doi:10.4141/p03-223

Long-term marketable yields of horticultural crops in southern Ontario in relation to seasonal climate

2005· article· en· W2030028258 on OpenAlexaffvenueabout
A.W. McKeown, J. Warland, Mary Ruth McDonald

Bibliographic record

VenueCanadian Journal of Plant Science · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsGrowing seasonCropYield (engineering)PrecipitationClimate changeEnvironmental scienceAgronomyGeographyBiologyEcologyMeteorology

Abstract

fetched live from OpenAlex

In Ontario, Canada, marketable yields of certain annual horticultural crops increased steadily from 1940 to the mid-1980s, then dramatically decreased and became highly variable. This was not seen in field crops such as soybean and corn. Standard climatological variables showed little correlation with yields of vegetable crops such as tomatoes, cabbage, cauliflower, onions and rutabagas. The number of hot days (defined as days with maximum temperature greater than 30°C) and number of days with precipitation were better correlated with cool-season vegetable crop yield. With these climate measures we identified a period of milder growing season climate, from 1961 to 1986, and there was a greater frequency of hot days before and after this period. This analysis shows that horticultural crops are more sensitive to specific synoptic events, such as periods of hot days, than to overall growing season climate, and their yields may provide an indicator of a changing climate. Key words: Horticultural yields, historical climate, weather

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)0.001
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 score0.016
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.026
GPT teacher head0.240
Teacher spread0.214 · 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

Citations46
Published2005
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Plant ScienceSame topicHorticultural and Viticultural ResearchFrench-language works237,207