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Record W2741013380

Insecticide Use Maintains Productivity of European Oilseed Rape Fields

2011· article· en· W2741013380 on OpenAlexaboutno aff
Leonard Gianessi, Ashley Williams

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect Resistance and Genetics
Canadian institutionsnot available
Fundersnot available
KeywordsRapeseedBiologyCropAgronomyProductivityPollenBotany
DOInot available

Abstract

fetched live from OpenAlex

Oilseed rape is an important crop in the European Union. Rapeseed oil is used in the production of cooking oils, margarines and salad dressings. After the oil has been pressed out, the solid remains of the rapeseed are used as animal feed. Oilseed rape is called “canola” in the U.S. and Canada. The name “rape” originated from the Latin word “rapum” meaning turnip. In Europe, oilseed rape is attacked by a wide range of insect pests. Insects attack the roots, stems, leaves, flowers, pods, and seeds of oilseed rape plants. In the years before development of chemical insecticides, yield losses due to insects in European rapeseed fields were significant. In Germany, a full yield of rapeseed was harvested only one year during the period from 1910 to 1939. Yield reductions from insect pests averaged 25% and amounted to as much as 50% in two years [1]. A recent survey determined that in 18 of 20 European countries, 50% or more of the rapeseed acres are sprayed with insecticides to control populations of blossom beetles (also called pollen beetles)[2]. In 8 of the countries, more than 90% of the rapeseed acres are sprayed for blossom beetles. Blossom beetles hibernate in the woods and migrate to rape fields attracted by the crop’s yellow flowers. The adults feed on pollen, and lay eggs in holes chewed in the base of the flower buds. An individual female lays up to 185 eggs. The eggs hatch and the larvae feed on the flower parts for up to three weeks [3]. Feeding by larvae results in buds falling from the plant, resulting in podless stalks and dramatically reduced yields [4].

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 score0.089
Threshold uncertainty score0.377

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 head0.028
GPT teacher head0.221
Teacher spread0.193 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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