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Record W2948833048 · doi:10.1021/acs.jafc.9b01432

Dimethylolurea as a Novel Slow-Release Nitrogen Source for Nitrogen Leaching Mitigation and Crop Production

2019· article· en· W2948833048 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2019
Typearticle
Languageen
FieldEngineering
TopicPolymer-Based Agricultural Enhancements
Canadian institutionsUniversity of New Brunswick
FundersMinistry of Science and Technology of the People's Republic of China
KeywordsLeaching (pedology)NitrogenEnvironmental scienceCrop productionAgronomyCropProduction (economics)Environmental chemistryChemistryAgricultureSoil scienceBiologyEcologyEconomicsSoil water

Abstract

fetched live from OpenAlex

Rapid hydrolysis of urea results in further fertilization frequency and excessive nitrogen (N) input. A modified urea, dimethylolurea (DMU), was synthesized in this study. The structure of the sample was characterized by Fourier transform infrared and nuclear magnetic resonance analysis, manifesting the formation of DMU. N release investigation confirmed that DMU enabling provided a gradual N supply. The N leaching experiment indicated that increasing the applied DMU significantly reduced the NH4+-N, NO3–-N, and total N leaching, compared with urea application alone. The application effect on maize and wheat was evaluated. The results revealed that singly applied DMU with 100% or 80% N input, irrespective of the amount, promoted crop yield and agronomic characteristic and N use efficiency (NUE) of maize and wheat, beyond urea with two split applications at the recommended rate. Thus, the potential availability of DMU was proven; this could be widely used in agricultural fields as a slow-release fertilizer.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.485

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 head0.005
GPT teacher head0.181
Teacher spread0.176 · 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