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Record W2583149222 · doi:10.21273/hortsci.40.4.1132a

Environmental Losses of Soil-applied Nitrogen Sources in Lowbush Blueberry Production

2005· article· en· W2583149222 on OpenAlexaff
David Percival, Gloria Thyssen, Kevin Sanderson, David L. Burton

Bibliographic record

VenueHortScience · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsAgriculture and Agri-Food CanadaNova Scotia Department of Agriculture
Fundersnot available
KeywordsDiammonium phosphateVolatilisationNitrogenAmmonia volatilization from ureaRandomized block designFertilizerUreaChemistryAmmonium sulfateHorticultureAmmoniumAgronomyNitrogen fertilizerAmmonium phosphateAnimal scienceBiology

Abstract

fetched live from OpenAlex

Environmental losses of soil-applied nitrogen fertilizers were examined during 2004 in commercial wild blueberry fields in the vegetative phase of production in Nova Scotia (NS) and Prince Edward Island (PE). A randomized complete-block experimental design with five treatments, five replications, a plot size of 8 × 6 m, and 2-m buffers between plots was used. Treatments consisted of a control (no fertilizer application) and nitrogen applications (N at 35 kg·ha -1 ) of ammonium sulphate (AS), urea (U), diammonium phosphate (DAP), and sulfur-coated urea (SCU). Nitrogen applications occurred on 19 May and 9 June at the Kemptown (NS) and Mount Vernon (PE) sites, respectively. Cumulative ammonia volatilization was determined through the use of open top chambers with volatilization samples collected on 1, 2, 5, 8, and 12 days after treatment application. In addition, leaf tissue and yield component data were collected. A significant volatilization treatment effect was present at the Kemptown site with the U and SCU treatments having volatilization rates that were 321% and 207% greater than the control, respectively. Therefore, results from this study indicate that volatilization losses are significant and site specific and can negatively influence blueberry growth.

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 score0.601
Threshold uncertainty score0.384

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 head0.016
GPT teacher head0.212
Teacher spread0.197 · 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
Published2005
Admission routes1
Has abstractyes

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