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Record W4285212756 · doi:10.5376/tgg.2022.13.0004

Effects of Density and Nitrogen Fertilizer on The Yield, Agronomic Traits and Photosynthetic Characteristics of

2022· article· en· W4285212756 on OpenAlexvenueno aff
Zhilong Wang, Ya-Xiong YU, Xiangmei Qiao, Zhiwei Wang, Jiasheng Cheng, Cheng Geng, Jinhua Yang

Bibliographic record

VenueTriticeae Genomics and Genetics · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsTiller (botany)SowingAgronomyPhotosynthesisRandomized block designTranspirationNitrogenFertilizerStomatal conductanceYield (engineering)ChlorophyllMathematicsChemistryBiologyHorticultureBotanyMaterials science

Abstract

fetched live from OpenAlex

In order to understand the optimal planting density and nitrogen fertilizer application amount of barley variety ‘Yundamai 12’, a two-factor randomized block experiment was designed to explore the effects of density, nitrogen fertilizer and their interactions on the yield, agronomic traits and photosynthetic parameters of ‘Yundamai 12’. The results showed that different densities had no significant effect on yield, but no effect on a single plant tiller. There were no significant effects of density and nitrogen application rates on four photosynthetic parameters and chlorophyll content. Dense nitrogen interaction did not affect yield, agronomic traits, stomatal conductance, intercellular CO 2  concentration, transpiration rate and chlorophyll significantly were not significant, but only had a significant effect on the net photosynthetic rate; the planting density was 850 000 hm 2 . The yield reached the maximum when the amount of nitrogen was 112.5 kg/hm 2 , which was the optimal density and nitrogen fertilizer scheme for ‘Yundamai 12’.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.174

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 head0.013
GPT teacher head0.184
Teacher spread0.170 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueTriticeae Genomics and GeneticsSame topicCrop Yield and Soil FertilityFrench-language works237,207