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Record W6940657496 · doi:10.1139/cjps2012-127

Effects of a seed treatment with a neonicotinoid insecticide on germination and freezing tolerance of spring wheat seedlings

2013· article· en· W6940657496 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsGerminationNeonicotinoidCropImidaclopridSeed treatmentSpring (device)Sowing

Abstract

fetched live from OpenAlex

Larsen, R. J. and Falk, D. E. 2013. Effects of a seed treatment with a neonicotinoid insecticide on germination and freezing tolerance of spring wheat seedlings. Can. J. Plant Sci. 93: 535-540. Spring wheat is a major crop in Canada, and in the western and northern regions of the production area early planting is required to ensure high yield, and high grain quality. This puts the crop at risk for exposure to early season cold or freezing temperatures. This study examined whether germination and freezing tolerance of seedlings of 11 spring wheat cultivars is affected by a seed treatment containing a neonicotinoid insecticide. As a result of the seed treatment, the number of germinating seeds was significantly higher for treated than untreated seed lots. There was also a slight (5.7%), but significant increase in freezing tolerance of treated plants based on the vigour of regrowth of seedlings exposed to -4°C in controlled freezing tests performed indoors. Differences in the response to the neonicotinoid insecticide treatment were observed between varieties. These preliminary results indicate that the seed treatment may be effective in improving germination and freezing tolerance of spring wheat as assessed in indoor screening tests. Further testing will be required to establish whether a positive effect can be confirmed in more variable outdoor environments and in other cereal crop types.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.994

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 head0.080
GPT teacher head0.199
Teacher spread0.119 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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