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Record W2037916638 · doi:10.2134/agronj2009.0102

Evaluation of Nitrogen Sources and Application Methods for Nitrogen‐Rich Reference Plot Establishment in Corn

2010· article· en· W2037916638 on OpenAlexaff
Huan Yu, Nicolas Tremblay, Zhijie Wang, C. Bélec, Gaihe Yang, Cynthia A. Grant

Bibliographic record

VenueAgronomy Journal · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsNitrogenPhotosynthesisUreaSowingChemistryAmmonium nitrateAgronomyAmmoniumNitrateZea maysChlorophyllEnvironmental scienceAnimal scienceBiology

Abstract

fetched live from OpenAlex

Five different N sources were compared with null N treatment to evaluate their performance for N‐rich reference plot establishment in corn ( Zea mays L.). The sources, including calcium ammonium nitrate (CAN), urea ammonium nitrate (UAN), polymer‐coated urea (PCU) and environmentally smart nitrogen (ESN) in 2007 and 2008, and urea (URE), were broadcast or in‐soil banded at the rate of 225 kg N ha −1 A greenhouse trial was also conducted with N applied as CAN, URE, and ESN. Net photosynthesis rate (P N ) and chlorophyll fluorescence parameters (Fv/Fm or Fv′/Fm′) were measured to assess N sources effects on corn photosynthesis. Relative photosynthetic capacity (RPC) and relative chlorophyll fluorescence capacity (RCFC) were calculated to evaluate the performance of N sources in N‐rich reference plot establishment. There were no differences in the release pattern of N from different sources that could lead to differences in RPC and RCFC during the period when N status diagnosis is normally performed. Hence, all sources were equally effective to establish N‐rich reference plots in our experimental conditions. It was also found that growers have the flexibility to either broadcast N at sowing or to band N along the rows at a later time after corn emergence.

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)0.003
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 score0.725
Threshold uncertainty score0.119

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.050
GPT teacher head0.327
Teacher spread0.277 · 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

Citations9
Published2010
Admission routes1
Has abstractyes

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