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Effect of Wheat Stem Sawfly Damage on Yield and Quality of Selected Canadian Spring Wheat

2007· article· en· W2179097579 on OpenAlexaffabout
Brian L. Beres, Héctor A. Cárcamo, J. R. Byers

Bibliographic record

VenueJournal of Economic Entomology · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsSawflyBiologySpring (device)AgronomyYield (engineering)HorticultureBotanyLarva

Abstract

fetched live from OpenAlex

The wheat stem sawfly, Cephus cinctus Norton (Hymenoptera: Cephidae), has reached outbreak status at most locations in the southern Canadian prairies. Solid-stemmed wheat, Triticum aestivum L., cultivars, which are less susceptible to damage, remain the primary management option. This article quantifies the effect of wheat stem sawfly damage on grain yield and quality at harvest and determines how cultivar selection affects harvest losses. Solid-stemmed cultivars were compared with hollow-stemmed cultivars and with blends of a 1:1 ratio of each. The hollow-stemmed cultivars with the exception of'McKenzie', which had intermediate levels of stem cutting, were all significantly more susceptible to stem cutting than solid-stemmed cultivars. Cultivar blends had lower damage but were still significantly higher than the solid-stemmed cultivars. The solid-stemmed 'AC Eatonia' and 'AC Abbey' had the lowest levels of stem cutting and ranked second and third overall for yield in 2001 and 2002. McKenzie ranked first, which reflects its yield potential in combination with its partial resistance to stem cutting. Lower cutting in AC Eatonia, AC Abbey, McKenzie, and the blend of AC Abbey/ McKenzie was significantly correlated with lower grain losses. Grain lost at harvest has major economic implications if sawfly pressure is moderate to high and susceptible cultivars predominate.

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)0.002
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 score0.243
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.031
GPT teacher head0.268
Teacher spread0.237 · 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

Citations51
Published2007
Admission routes2
Has abstractyes

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