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Record W1989189391 · doi:10.4141/p00-145

Three-component barley mixtures: Ratio effects in replacement series

2001· article· en· W1989189391 on OpenAlexvenueaboutno aff
P. E. Juskiw, J. H. Helm, P. A. Burnett

Bibliographic record

VenueCanadian Journal of Plant Science · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsnot available
Fundersnot available
KeywordsHordeum vulgareCultivarMonocroppingYield (engineering)SeedingMathematicsAgronomyPoaceaeBiologyMaterials scienceEcology

Abstract

fetched live from OpenAlex

Within a species, cultivar mixtures may offer yield and quality advantages if the cultivars have complementary abiotic and biotic stress tolerances. This study was conducted at Botha, Lacombe and Olds, Alberta, from 1992 to 1994 to determine the effect of relative seeding ratios on yield and other traits of 16 three-component barley (Hordeum vulgareL.) mixtures of Virden:Abee:Tukwa all grown at a standard seeding rate of 250 seeds m –2 . Grain yields of these mixtures fell between the yields of the monocrops, with yields of the 20:40:40 and 50:30:20 mixtures being higher than expected based on the weighted mean yields of the monocrops. When stability of yield was measured using ranking or regression analyses, several mixtures had desirable combinations of high yields and good stability with the 20:40:40 and the 40:20:40 mixtures being identified using either method. Test weights, kernel weights, percent thins, protein contents, and disease levels of the mixtures were intermediate to the monocrops; while lodging levels were as low as the best monocrop. As the proportion of any one cultivar in the mixture increased, the traits it brought to the mixture also increased. These mixtures had no yield advantage over growing a high yielding monocrop. Key words: Barley, Hordeum vulgare L., mixtures, cultivar, yield, tolerance, stress

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.985

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 communication0.0000.001
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 head0.017
GPT teacher head0.208
Teacher spread0.192 · 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 designObservational
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

Citations19
Published2001
Admission routes2
Has abstractyes

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