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Record W2555013861 · doi:10.1139/cjps-2016-0071

Crop rotation and intercropping with marigold are effective for root-knot nematode (Meloidogyne sp.) control in angelica (Angelica sinensis) cultivation

2016· article· en· W2555013861 on OpenAlexvenueno aff
Gui-hua Xie, Hua-dong Cui, Ying Dong, Xiao-qiang Wang, Xiaofei Li, Ren-ke Deng, Yang Wang, Yong Xie

Bibliographic record

VenueCanadian Journal of Plant Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsnot available
Fundersnot available
KeywordsIntercroppingAngelica sinensisTagetesCrop rotationBiologyAgronomyCropHorticultureMathematics

Abstract

fetched live from OpenAlex

This study was aimed to investigate the control efficiency of crop rotation (CR) and intercropping systems for root-knot nematodes (Meloidogyne sp.) in angelica (Angelica sinensis). Plants of angelica were intercropped with marigold (Tagetes erecta) plants in row-intercropping (RI) and plant-intercropping (PI) models in 2013. The continuous cropping (CC) model and sole cropping (SC) were used as the controls. The density change rate of nematodes in CR models (−83.63% and −72.61%) was lower than those in CC models. The CR model in 2013 showed the highest nematode control efficiency (44.83%), angelica yield (199.49 kg plot−1), and output value (65971.50 yuan Renminbi ha−1), and were the highest amongst the five models. The RI model showed a lower density change rate of nematodes (−23.34%) and a higher control efficiency for disease (36.63%) compared with the SC model. Both the rotation and intercropping models were efficient for controlling nematodes in angelica, and the rotation model was more effective than the intercropping models.

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 score0.514
Threshold uncertainty score0.979

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 science0.0000.000
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 head0.195
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

Citations25
Published2016
Admission routes1
Has abstractyes

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