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Record W6884624270 · doi:10.1139/cjps2011-201

Slow-release nitrogen fertilizer in carrot production on Prince Edward Island

2012· article· en· W6884624270 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2012
Typearticle
Languageen
FieldEngineering
TopicPolymer-Based Agricultural Enhancements
Canadian institutionsnot available
Fundersnot available
KeywordsFertilizerNitrogen fertilizerYield (engineering)CropNitrogenDaucus carotaProduction (economics)Crop production

Abstract

fetched live from OpenAlex

Sanderson, K. R. and Fillmore, S. A. E. 2012. Slow-release nitrogen fertilizer in carrot production on Prince Edward Island. Can. J. Plant Sci. 92: 1223-1228. The impact of nitrogen management using slow-release fertilizers has not been examined for carrot (Daucus carota L.) production in Atlantic Canada. To assess the effects of such products, we evaluated five slow-release fertilizers over a 3-yr period. Treatments consisted of sulphur coated urea (SCU) (42-0-0), isobutylidene diurea (IBDU) (31-0-0), Nutralene® (methylene urea) (40-0-0), Sirflor® (urea formaldehyde) (38-0-0), UFLEXX™ (urea, dicyandiamide, N-(n-butyl) thiophosphoric triamide) (46-0-0) compared with industry standard (ammonium nitrate) (34-0-0). All slow-release fertilizer treatments supplied N at 50 kg N ha-1 pre-plant. Slow-release fertilizer treatments were compared with the industry standard of 50 kg N ha-1 pre-plant plus 50 kg N ha-1 as a top dress. UFLEXX™ increased biological and marketable yield by 7.5 and 18.7%, respectively, compared with the industry standard. Sirflor® produced the lowest yield. GreenSeeker® NDVI reading and LECO N were correlated at the mid-season sample. GreenSeeker® technology has potential as an in-field crop health assessment tool, however, further research is required to develop GreenSeeker® algorithms appropriate for carrot production in Prince Edward Island.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score1.000

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)0.0000.001

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 head0.137
GPT teacher head0.225
Teacher spread0.088 · 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.

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
Published2012
Admission routes1
Has abstractyes

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