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Record W2003075074 · doi:10.2134/agronj2005.0295

Economic Value of Polymer Seed Coat for Fall‐Seeded Canola

2007· article· en· W2003075074 on OpenAlexafffund
Bharat Mani Upadhyay, Elwin G. Smith, George W. Clayton, K. Neil Harker, John T. O’Donovan, Robert E. Blackshaw

Bibliographic record

VenueAgronomy Journal · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNitrogen and Sulfur Effects on Brassica
Canadian institutionsAgriculture and Agri-Food Canada
FundersAlberta Crop Industry Development FundU.S. Department of Agriculture
KeywordsSeedingCanolaAgronomyCultivarBiologyBrassica rapaSowingYield (engineering)BrassicaGermination

Abstract

fetched live from OpenAlex

Applying a polymer seed coat (PSC) to canola (Brassica napus L. and B. rapa L.) seed could be economically beneficial for dormant fall seeding. Field trials at three locations and across 3 yr were used to evaluate the effect of seeding date, use of a PSC, and canola cultivar on crop yield and net returns. The net returns were used to estimate the economic value of using a PSC for three locations, two seeding dates, and four canola cultivars. Net returns differed across locations, and cultivar differences occurred in one of the three locations. Net returns were higher for late‐fall seeding, compared with early‐fall seeding, with or without the use of a PSC. The value of a PSC was generally positive for early‐fall seeding and negative for late‐fall seeding. However, at a location prone to midwinter mild spells during which seed germination could occur, the PSC generally had no value. Despite the PSC having a positive value for early‐fall dormant seeding, its potential use will be limited because early‐fall seeding with a PSC had lower net returns than late‐fall seeding without a PSC.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.240
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2007
Admission routes2
Has abstractyes

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