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Record W2011094446 · doi:10.2134/agronj2010.0337

Physiological Response of Chinese Cabbage to Intercropping Systems

2011· article· en· W2011094446 on OpenAlexaff
Hongjiao Cai, Minsheng You, Krista Ryall, Shiyou Li, Hongyi Wang

Bibliographic record

VenueAgronomy Journal · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsIntercroppingSugarBrassicaLactucaAgronomyBiologyNitrateMonocultureSativumChlorophyllReducing sugarHorticultureFood science

Abstract

fetched live from OpenAlex

The physiological indices of Chinese cabbage( Brassica chinensis L.) grown under different intercropping systems used for this study included total soluble protein content, soluble sugar content, reducing sugar content, nitrate content, and pigment concentration. The objective of the present study is to discover the physiological level changes in Chinese cabbage in intercropping systems. The intercropping systems studied involved Chinese cabbage‐ garlic ( Allium sativum L.) (CG), and Chinese cabbage‐lettuce ( Lactuca sativa L.) (CL). Chinese cabbage monoculture served as control (CK). Overall, higher mean soluble protein content and nitrate content were found in Chinese cabbage grown in the intercropping systems than those in CK. Significantly higher chlorophyll a content was found in cabbages from CL than CK during the latter half of the growing season. No significant difference in soluble sugar concentrations was found in CG and CL, as compared with CK. Reducing sugar content varied over the growing period of the Chinese cabbage in CG and CL. These results suggest that Chinese cabbage intercropped with noncrucifer plants increase the plant nutrient content.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.690

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 head0.053
GPT teacher head0.252
Teacher spread0.200 · 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

Citations10
Published2011
Admission routes1
Has abstractyes

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