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Record W6922123537 · doi:10.1139/cjps2012-148

Application of seed-row potash to spring wheat grown on soils with high available potassium levels

2013· article· en· W6922123537 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2013
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsnot available
Fundersnot available
KeywordsPotashSpring (device)FertilizerPotassiumSoil waterCultivar

Abstract

fetched live from OpenAlex

Karamanos, R., Flore, N. A. and Harapiak, J. T. 2013. Application of seed-row potash to spring wheat grown on soils with high available potassium levels. Can. J. Plant Sci. 93: 271-277. Two experiments were conducted at numerous locations across western Canada from 1990 to 1994 to ascertain the response of hard red spring (CWRS) wheat (Triticum aestivum L.) to seed-row applied K fertilizers. Soil test K levels at all sites (location×year combinations) exceeded the critical level for western Canada of 125 mg NH4OAc-extractable K kg-1, the concentration below which the probability of a K deficiency is high. In the first experiment, days to maturity and yield for wheat were statistically similar whether or not KCl was applied in the seed row. When the cultivar Roblin was grown significant (P<0.05) yield reductions as a result of seedrow placing low rates of potassium fertilizers were obtained. The second experiment showed that maturity and yield did not respond to treatments including KCl, K2SO4, or CaCl2 fertilizer applied in seed row. Furthermore, spring wheat responses for any of the preceding treatments were not different relative to the control (no K fertilizer applied). These findings bring into question the benefit of seed-row K fertilizer application to hard red spring (CWRS) wheat production on K-sufficient soils in western Canada.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.147
Threshold uncertainty score1.000

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)0.0020.006

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 head0.189
GPT teacher head0.247
Teacher spread0.057 · 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; both teacher heads agree on what is shown here.

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
Published2013
Admission routes1
Has abstractyes

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