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Effects of Different Applying Fertilization Patterns on Soil Microorganisms, Organic Carbon and Respiration Flux in Maize Grown under Ridge Cultivation

2012· article· en· W2018715416 on OpenAlexaff
Yun Xian Dai, Jing Hui Liu, Li Jun Li, Aodungerile Chen, Li‐Gang Wang, Lei Qing Chen, Qin Chen, S. N. Acharya

Bibliographic record

VenueAdvanced materials research · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
FundersPeople's Government of Jilin Province
KeywordsFertilizerBiomass (ecology)Soil carbonTotal organic carbonSoil respirationAgronomyRespirationHuman fertilizationOrganic fertilizerChemistryTillageYield (engineering)NitrogenCarbon fibersAnimal scienceEnvironmental scienceEnvironmental chemistrySoil waterSoil scienceBotanyBiologyMaterials science

Abstract

fetched live from OpenAlex

The effects of three different fertilization patterns including single chemical fertilizer(DH), chemical-organic fertilizer(HY), and single organic fertilizer (DY),no fertilizer(CK) on soil microbial biomass, soil organic carbon content, yield and soil respiration flux in ridge cultivation maize fields in the West Liaohe region were studied. The results showed that soil microbial biomass, organic carbon content, yield and soil respiration flux under HY were higher than the rest of other treatments in 2005 and 2006. The varying order of soil microbial total biomass performed as HY>DY>DH>CK on the ridge-tillage maize farmland. The soil total nitrogen concentration, organic carbon concentration and respiration of HY were significantly higher than the rest of other treatments (p HY> DH > CK; the order of biological yield was DH>HY>DY> CK; the order of grain yield was HY>DH>DY> CK and the economic coefficient of HY was the highest in both years.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

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 head0.030
GPT teacher head0.286
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2012
Admission routes1
Has abstractyes

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