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Record W2909913377 · doi:10.2134/agronj2017.08.0471

Nitrogen Supply from Green Manure Enhanced with Increased Tillage Frequency: A Note

2019· article· en· W2909913377 on OpenAlexafffund
Leonardo León Castro, Joann K. Whalen

Bibliographic record

VenueAgronomy Journal · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGreen manureManureAgronomyChemistryAmmoniumAvenaNitrogenSativumBiology

Abstract

fetched live from OpenAlex

Core Ideas Ion exchange membranes were tested for in situ evaluation of soil mineral N. More tillage passes increased green manure decomposition and soil mineral N. Ion exchange membrane were a good indicator of arugula N uptake. Pea–oat green manure supplied up to 20% of the N required by arugula. Tillage practices influence the decomposition of green manure and could be adjusted to synchronize the N supply from residues with crop N demand. This note evaluated ion exchange membranes (IEM) as an in situ tool for monitoring ammonium (NH 4 + ) and nitrate (NO 3 − ) dynamics after pea ( Pisum sativum L.)–oat ( Avena sativa L.) green manure was incorporated by one, two, or four passes of a rototiller. Mineral N from IEM and in 2 M KCl soil extracts was related to the cumulative N assimilated by arugula ( Eruca sativa L.) during a 6‐wk period. Greater tillage intensity increased the IEM‐NO 3 − −N concentration on ion exchange membranes significantly, from 1.94 to 18.7 µg cm −2 wk −1 , and the N supplied from green manure increased arugula N uptake significantly. The IEM‐N were as reliable as the soil chemical extractant in evaluating the mineral N released from green manure under field conditions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score0.998

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)0.0030.000

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 head0.007
GPT teacher head0.179
Teacher spread0.173 · 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 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

Citations7
Published2019
Admission routes2
Has abstractyes

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