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Record W2479172977 · doi:10.5539/mas.v10n12p57

Vertical Profile of Leaf Nitrogen Distribution at Different Densities of Plant And Different Levels of N Fertilizer on Corn Hybrids Canopy

2016· article· en· W2479172977 on OpenAlexvenueno aff
Soraya Ghasemi, A Kochaki, Mehdi Nassiri Mahallati, R Choukan

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsCanopyHybridNitrogenSowingAgronomyPhotosynthesisFertilizerPopulationPlant canopyAnimal scienceHorticultureBiologyChemistryBotany

Abstract

fetched live from OpenAlex

In this study, the distribution of leaf nitrogen of two corn hybrids during the growing season, in response to the density and N supply were studied. There were two corn hybrids (S.C. 704 and maxima (MV 524)) under three levels of nitrogen: Recommended (300 kg N/ha); RDN plus 25% (375 kg N/ha); RDN plus 50% (450 kg N/ha) and were three levels of population. Recommended (RDN) (7 plants m-2); RDN plus 10% (7.7 plants m-2); RDN plus 20% (8.4 plants m-2).Regular samplings were made from 40 days after sowing until crop maturity. Every 2 weeks, a 1 m2 sample of the canopy was cut in four successive vertical layers of equal thickness. Leaf area and N concentration (%) in each layer were measured. The vertical N gradient became more marked with ongoing vegetative development. The whole canopy photosynthetic benefit that would accrue from maintaining the N gradient is likely to be accentuated. The rate of decline in leaf N concentration in a layer was not related to either the initial concentration in the leaves within the layer.

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 score0.430
Threshold uncertainty score0.307

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 studies0.0000.001
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 head0.027
GPT teacher head0.214
Teacher spread0.187 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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