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Record W3208672170 · doi:10.3390/plants10112364

Nitrogen Use Efficiency in Parent vs. Hybrid Canola under Varying Nitrogen Availabilities

2021· article· en· W3208672170 on OpenAlexafffundabout
Shanay Williams, Sally Vail, Melissa Arcand

Bibliographic record

VenuePlants · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNitrogen and Sulfur Effects on Brassica
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Saskatchewan
FundersAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of CanadaMinistry of Agriculture - Saskatchewan
KeywordsCanolaAgronomyChernozemBrassicaNitrogenRhizosphereRapeseedCropBiologySoil waterChemistry

Abstract

fetched live from OpenAlex

). While advancements in above-ground agronomic practices have improved NUE, research on soil and below-ground processes are limited. Plant NUE-and its components, N uptake efficiency (NUpE), and N utilization efficiency (NUtE)-can be further improved by exploring crop variety and soil N cycling. Canola parental genotypes (NAM-0 and NAM-17) and hybrids (H151857 and H151816) were grown on a dark brown chernozem in Saskatchewan, Canada. Soil and plant samples were collected at the 5-6 leaf stage and flowering, and seeds were collected at harvest maturity. Soil N cycling varied with phenotypic stage, with higher potential ammonium oxidation rates at the 5-6 leaf stage and higher urease activity at flowering. Seed N uptake was higher under higher urea-N rates, while the converse was true for NUE metrics. Hybrids had higher yield, seed N uptake, NUtE, and NUE, with higher NUE potentially owing to higher NUtE at flowering, which led to higher yield and seed N allocation. Soil N cycling and soil N concentrations correlated for improved canola NUE, revealing below-ground breeding targets. Future studies should consider multiple root characteristics, including rhizosphere microbial N cycling, root exudates, and root system architecture, to determine the below-ground dynamics of plant NUE.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.845

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 head0.017
GPT teacher head0.240
Teacher spread0.224 · 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

Citations20
Published2021
Admission routes3
Has abstractyes

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