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Record W1964784307 · doi:10.4141/p03-002

Cost of crop losses in processing tomato and cabbage in southwestern Ontario due to insects, weeds and/or diseases

2004· article· en· W1964784307 on OpenAlex
J. H. Tolman, D. G. R. McLeod, C. R. Harris

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Plant Science · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Management Techniques
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsLycopersiconYield (engineering)CropBrassicaBrassica oleraceaBiologyAgronomyCrop yieldPEST analysisCapitataIntegrated pest managementHorticulture

Abstract

fetched live from OpenAlex

The relative importance of insects, weeds and diseases to yield losses in processing tomato (Lycopersicon esculentum Mill.) and cabbage (Brassica oleracea L. var. capitata L.) was measured by comparing yields in the presence and absence of appropriate control programs. In the absence of any pest control, average crop losses exceeded 80% in both crops. Average yield losses due to weeds alone approached 80% in processing tomato and 60% in cabbage. Insects alone did not significantly reduce yield of processing tomato in either year. In the absence of insect control, significant yield loss in cabbage approached 50% in only one year. When diseases were not controlled, yield of processing tomato declined significantly by nearly 30% in one trial. Failure to control disease had no significant impact on cabbage yield in this study. Monetary losses and costs of each management program were calculated. Key words: Tomato, cabbage, yield loss, insects, weeds, diseases

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.

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 score0.781
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.224
Teacher spread0.199 · 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