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Record W2160032870 · doi:10.4141/p06-138

Differential response of weed and crop species to potassium and sulphur fertilizers

2007· article· en· W2160032870 on OpenAlexvenueno aff
Cynthia A. Grant, D. A. Derksen, Robert E. Blackshaw, T. Entz, H. H. Janzen

Bibliographic record

VenueCanadian Journal of Plant Science · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsnot available
Fundersnot available
KeywordsAgronomyWeedBiologyFertilizerCanolaBiomass (ecology)PotashCropPopulation

Abstract

fetched live from OpenAlex

Fertilization may affect the relative competitive ability of weeds and crops if the growth response to fertilizer differs among species. Greenhouse studies were conducted to evaluate the relative biomass yield response of 19 weed species and the crops canola (Brassica napus L.) and spring wheat (Triticum aestivum L.) to potassium (K) and sulphur (S) fertilization. Seven weed species showed a significant increase in biomass production at 6 wk with K fertilization, but biomass production of canola and wheat did not increase with K addition. Sulphur fertilizer increased the biomass production of canola, flixweed (Descurainia sophia L.) and wild mustard (Sinapis arvensis L.), three of the four Cruciferae species evaluated, as well as that of hairy nightshade (Solanum sarrachoides Sendtner), round-leaved mallow (Malva pusilla Sm.) and stork’s-bill [Erodium cirutarium (L.) L’Her. Ex. Ait.]. Sulphur fertilizer did not increase the biomass production of spring wheat or of any grass weed species. The data from the greenhouse study support the concept that fertilization may shift the relative competition between a crop and the weed population if the weeds are more responsive than the crop to the type of fertilizer applied. However, this concept should be tested under field conditions. Key words: Integrated weed management, sulphur, potassium

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)0.001
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 score0.982
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.208
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

Citations27
Published2007
Admission routes1
Has abstractyes

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