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Record W2376121254

Impacts of No-tillage on Earthworm and Soil bulk Density in Black Soil in Northeast China

2007· article· en· W2376121254 on OpenAlexaff
Yang Xue-ming

Bibliographic record

VenueSystem Sciences and Comprehensive Studies in Agriculture · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsEarthwormTillagePloughBulk densityAgronomyEnvironmental scienceRandomized block designConventional tillageNo-till farmingSoil horizonMathematicsSoil waterSoil scienceSoil fertilityBiology
DOInot available

Abstract

fetched live from OpenAlex

The tillage experiment trials were established in Dehui County,Jilin Province in 2001.The impacts of no-tillage(NT) and moldboard plow(MP) on the number of earthworms and soil bulk density of a black soil in Northeast China were studied.The tillage experiment was arranged in a randomized complete block design with four replicates.Each tillage plot was split into sub-plots with different corn-soybean rotations.Earthworm was counted two times when the corn has six leaves and in late August or early September.The data of earthworms and soil bulk density from 2004 to 2006 was analyzed using SAS.The results showed that the differences of earthworms under tillage,crop and time were significant.The results showed that the number of earthworms under NT,soybean and the first time were respectively 2.4,1.6 and 1.9 times as many as those under MP,corn and the second time.There were no significant differences of soil bulk density between tillage treatments and crops.NT increased bulk density of soil layer of 5 cm-10cm,but there was no significant differences between NT and MP.Studies showed that NT compared to MP could increase the number of earthworms and could not result in the soil becoming hard.

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)0.001
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 score0.093
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.250
Teacher spread0.229 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

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