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Record W2076007426 · doi:10.2134/agronj2010.0494

Barley Productivity Response to Polymer‐Coated Urea in a No‐Till Production System

2011· article· en· W2076007426 on OpenAlexaffabout
Robert E. Blackshaw, Xiying Hao, K. Neil Harker, John T. O’Donovan, Eric N. Johnson, Cecil Vera

Bibliographic record

VenueAgronomy Journal · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsAgronomyHordeum vulgareCultivarUreaWeed controlYield (engineering)FertilizerWeedBiomass (ecology)BiologyPoaceaeChemistryMaterials science

Abstract

fetched live from OpenAlex

Farmers are interested in more cost‐efficient and environmentally sound fertilization programs in field crops. A multi‐site study on the Canadian prairies was conducted to determine the effect of polymer‐coated urea (Environmentally Smart Nitrogen, ESN) compared with urea on weed management and barley ( Hordeum vulgare L.) yield and quality. Treatments included a semi‐dwarf and tall barley cultivar, polymer‐coated urea (ESN) and urea, 100 and 150% of soil test N fertilizer rates, and 50 and 100% of registered herbicide rates. Treatments were applied to the same plots in four consecutive years. Barley yield was greater with semi‐dwarf compared with tall barley in 13 of 20 site‐years but weed biomass was greater in 7 of 18 site‐years with the semi‐dwarf cultivar. The 150% N fertilizer rate increased yield of both cultivars in 9 of 20 site‐years and of the semi‐dwarf cultivar in four additional site‐years. Barley yield was often similar with ESN and urea but ESN increased barley yield in three site‐years at both N rates, two additional site‐years at the 150% N rate, and one further site‐year with semi‐dwarf barley. Barley grain protein concentration was greater with ESN than with urea in 8 of 20 site‐years. Information gained in this study will be used to advise growers on improved barley production practices.

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 score0.959
Threshold uncertainty score0.303

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 head0.021
GPT teacher head0.203
Teacher spread0.182 · 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

Citations14
Published2011
Admission routes2
Has abstractyes

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