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Record W1620287070

Response of the black soil enzyme activities to different fertilizer applications.

2011· article· en· W1620287070 on OpenAlexaff
Zhidan Zhang, Chunli Li, Hongbin Wang, Zhao Lan-po, Yang Xue-ming

Bibliographic record

VenueJournal of the South China Agricultural University · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsUreaseFertilizerInvertaseOrganic fertilizerAgronomyChemistryTillageSoil fertilityEnzyme assayEnvironmental scienceSoil waterEnzymeBiologyBiochemistrySoil science
DOInot available

Abstract

fetched live from OpenAlex

On the base of field experimentation and sampling,the status and diversity of enzyme activities relevant to cycle of C,N,P in the black soil from the Hailun Agroecological Experimental Station were tested.The results showed that soil enzyme acted as a biological catalyst in soil cycle,and its variation could be used as an indicator for soil fertility.The application of different organic and inorganic fertilizers significantly affected the soil enzyme activities under the field conditions.N fertilizer could increase invertase and protease activities,and organic fertilizer increased the organic contents and urease activity significantly.Urease activities were significantly correlated with different fertilizer applications and crops,and extremely significantly correlated with the interaction among crops and fertilizer application,which showed that urease activities could be used as the characteristic of fertilizer level under different crops and fertilizer applications.The activities of invertase,protease,phosphatase and urease under waste land were less than those of the continuous tillage,which indicated that catalase was less sensitive to tillage than the other four kinds of enzyme.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.172

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 science0.0010.000
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 head0.167
Teacher spread0.155 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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