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Record W6922258612 · doi:10.1139/cjps2012-150

Nitrogen fertilizer product and timing alternatives exist for forage production in the Peace region of Alberta

2013· article· en· W6922258612 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsForageFertilizerUreaAmmonium nitrateNitrateProfit (economics)Bromus inermisNitrogen

Abstract

fetched live from OpenAlex

Karamanos, R. E. and Stevenson, F. C. 2013. Nitrogen fertilizer product and timing alternatives exist for forage production in the Peace region of Alberta. Can. J. Plant Sci. 93: 151-160. Four different N sources [ammonium nitrate (NIT), urea (UR), polymer-coated urea (PCU), and N-(n-butyl) thiophosphoric triamide-treated urea (AGR)] were applied to stands of pure meadow bromegrass (Bromus beibersteinii L.) or a 50:50 smooth bromegrass (Bromus inermis L.)-alfalfa (Medicago sativa L.) mixture in late fall and early spring at four N rates (0, 60, 80 and 100 kg N ha-1) over a 3-yr (2003-2005) period. The N treatments generally increased forage responses, but the response net revenue to N treatment was rarely positive and at times was negative, especially for PCU. On average, PCU resulted in lower yield and protein concentration, lesser N efficiency, and lesser profit relative to other forms of N. This difference was more pronounced in the spring and was less notable at Rycroft, the location with the bromegrass-alfalfa mixture. Also, greater N fertilizer rates increased the yield, protein concentration, total N uptake, and profit for all fertilizer forms. The exceptions to the preceding were N fertilizer rate did not affect forage responses for PCU and at the location with the brome-alfalfa mixture. Urea or AGR provided satisfactory agronomic alternatives to ammonium nitrate when applied in early spring at sufficient rates.

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 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 score0.908
Threshold uncertainty score0.216

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)0.0000.000

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 head0.333
GPT teacher head0.262
Teacher spread0.071 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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