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Ecological Effects of Re-Used Film Mulching during Fallow Period of Cropland

2012· article· en· W2056813103 on OpenAlexaff
Jian Guo Shi, Jing Hui Liu, Bao Ping Zhao, Shao Xia Xue, Li Jia, S. N. Acharya, Qin Chen, Ya Fei Yan, Cai Ping Gao

Bibliographic record

VenueAdvanced materials research · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicIrrigation Practices and Water Management
Canadian institutionsAgriculture and Agri-Food Canada
FundersInner Mongolia Agricultural UniversityInner Mongolia University
KeywordsMulchPlastic filmSunflowerEnvironmental scienceMoistureWater contentAgronomyMaterials scienceBiologyComposite materialGeology

Abstract

fetched live from OpenAlex

Aiming at reducing agricultural pollution caused by plastic film, the effects of re-used plastic film mulching on soil moisture, temperatures and soil erosion during fallow period of cropland were studied, the study also investigated plastic film residue and sunflower’s yield, which compared with new plastic film mulching and bare field in Hetao region. The results showed that, compared with bare field, (1) the average soil moisture in 0~100 cm of re-used film mulching increased 2.1%, the water storage capacity in 0~100 cm and 0~20 cm increased 24.8~33.0mm and 14.9~15.5mm separately. (2) The soil average temperature significantly increased 3.1~2.8°C, especially soil temperature in 5 cm and 10 cm increased 1.7~4.6°C and 2.8~4.1°C separately (p < 0.05). (3) The amount of soil erosion significantly reduced by 68.77%. (4) Compared with new plastic film mulching, the residual plastic film of re-used film mulching cropland significantly reduced 50%, and the film use efficiency increased 1 time. (5) In addition, compared with bare field, sunflower yield of re-used film mulching was significantly increased 11.4% in 2010 and 16.8% in 2011 (p < 0.01), and compared to the new film treatment, the yield decreased 3.3% in 2010 and 2.6% in 2011 (p < 0.05). So re-used plastic film mulching can improve the ecological environment of cropland in fallow period, reduce the film pollution and increase grain yield.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.323
Teacher spread0.284 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

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