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Record W3000372169 · doi:10.1002/agj2.20080

Nitrogen increased aboveground biomass of <i>Leymus chinensis</i> grown in semi‐arid grasslands of inner Mongolia, China

2020· article· en· W3000372169 on OpenAlexaff
Yuting Bai, Ruirui Yan, Michael P. Schellenberg, Hong Wang, Guodong Han, Ruiyang Zhang, Jun Zhang, Zhijun Wei

Bibliographic record

VenueAgronomy Journal · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsSurrey Memorial HospitalAgriculture and Agri-Food Canada
FundersMinistry of Education of the People's Republic of China
KeywordsLeymusNitrogenBiomass (ecology)BiologyAgronomyStem-and-leaf displayAridInner mongoliaGrasslandBotanyNutrientHorticultureChemistryChinaEcology

Abstract

fetched live from OpenAlex

Abstract There is controversy over whether the addition of nitrogen (N) is the key to the rapid growth of Chinese rye grass Leymus chinensis (Trin.) Tzvel. in natural, semiarid grasslands. We investigated yearly impact of various N additions (0, 91, 183, and 274 kg N ha −1 ) on the relationships between nutrient traits (plant carbon [C], N, and phosphorous [P]), morphological traits (plant height, leaf number, leaf length, leaf width, stem length and stem diameter) and aboveground biomass in L. chinensis . Results showed that most of the growth characteristics of L. chinensis increased with N rate except for leaf number and stem length. Nitrogen addition increased aboveground biomass, plant height, leaf length and stem length of L. chinensis , which was related to high precipitation during the critical period for the growth of L. chinensis . Nitrogen addition increased the N concentration and N to P ratio of L. chinensis tissue, but decreased the C to N ratio in the leaf and stem of L. chinensis . Compared to the control, N addition increased C and N concentrations and the N to P ratio, but decreased P concentration and the C to N ratio.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.194
Teacher spread0.184 · 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 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

Citations10
Published2020
Admission routes1
Has abstractyes

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