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Record W3125180059 · doi:10.1093/jee/toaa323

Compensatory Abilities of Canola in Response to Swede Midge (Diptera: Cecidomyiidae) Damage

2020· article· en· W3125180059 on OpenAlexaffabout
Matthew J. Muzzatti, Boyd A. Mori, Rebecca H. Hallett

Bibliographic record

VenueJournal of Economic Entomology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect Resistance and Genetics
Canadian institutionsUniversity of AlbertaUniversity of GuelphCarleton University
FundersMinistry of Agriculture, Food and Rural Affairs
KeywordsCecidomyiidaeBiologyMidgeCanolaLarvaBotanyAgronomy

Abstract

fetched live from OpenAlex

Swede midge (Contarinia nasturtii Kieffer) is an invasive, economic pest of canola (Brassica napus L.) that threatens production throughout Canada. Swede midge has up to four overlapping generations, placing canola at risk of multiple infestations in the field. The relationship between single and multiple swede midge infestations at different canola stages, and the resulting impacts on yield, are unknown. Laboratory experiments were conducted to evaluate the impact of single and multiple infestations of swede midge on four plant stages of canola: three-leaf, seven-leaf, primary bud, and secondary bud. Previously, the seven-leaf and primary bud stages were considered vulnerable to swede midge, but we determined that the secondary bud stage is also vulnerable. Evidence of compensation by canola in response to herbivory by swede midge was discovered. Compensation occurred mainly through increased production of tertiary racemes and pods and was maximized with exposure of 4.5-6.5 female midges per plant. Although compensation may increase potential yield, it also results in uneven crop maturation resulting in delayed harvest and yield loss. Consequently, to prevent delays in maturation and harvest, insecticides should be applied before these densities are reached.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.261
Teacher spread0.247 · 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 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

Citations4
Published2020
Admission routes2
Has abstractyes

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