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Record W6884653060 · doi:10.1139/cjps2011-088

When is fall feeding by flea beetles (Phyllotreta spp., Coleoptera: Chrysomelidae) on canola (Brassica napus L.) a problem?

2012· article· en· W6884653060 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNitrogen and Sulfur Effects on Brassica
Canadian institutionsnot available
Fundersnot available
KeywordsCanolaFleaFlea beetleBrassicaCultivarPest control

Abstract

fetched live from OpenAlex

Soroka, J. J. and L. F. Grenkow. 2012. When is fall feeding by flea beetles (Phyllotretaspp., Coleoptera: Chrysomelidae) on canola (Brassica napusL.) a problem? Can. J. Plant Sci. 92: 97-107. Two cultivars of Brassica napus canola were seeded in mid-May and early June in three field experiments in each of 3 yr near Saskatoon, Saskatchewan, to determine the effects of late-season flea beetle feeding on seed yields. In the first experiment, canola was sprayed with insecticide late in the summer to eliminate naturally-infesting flea beetles. In the second, 1×1×1.5 m screen cages were placed over early- and late-seeded canola at flowering and infested with flea beetles as canola matured. In the third investigation, sleeve cages were placed over individual plants and infested with 100 flea beetles. Flea beetles had no detrimental effects on early-seeded canola in any experiment, but did affect seed yields of late-seeded plots in some trials. Over two cultivars in 1 year, late-seeded plants in cube cages infested with about 350 flea beetles per plant when lower pods were turning from translucent to green in colour reduced yield by 241 kg ha-1 over control yields. Seed weights in these late-seeded plots were decreased from 2.68 g per 1000 seeds in uninfested cages to 2.44 g per 1000 seeds in infested cages. Populations of 100 flea beetles per plant in sleeve cages had no effect on harvest parameters in any seeding date or year.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.093
GPT teacher head0.236
Teacher spread0.143 · 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.

Study designBench or experimental
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
Published2012
Admission routes1
Has abstractyes

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