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Record W2182456230 · doi:10.21273/hortsci.38.2.179

Rust-spotted North American Ginseng Roots: Phenolic, Antioxidant, Ginsenoside, and Mineral Nutrient Content

2003· article· en· W2182456230 on OpenAlexaff
Cindy Campeau, John Proctor, Chung‐Ja C. Jackson, H.P. Vasantha Rupasinghe

Bibliographic record

VenueHortScience · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGinseng Biological Effects and Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsGinsengRust (programming language)Gallic acidChemistryGinsenosideAntioxidantAscorbic acidDry weightBotanyNutrientHorticultureTraditional medicineFood scienceBiologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

Rusty root is a major problem in ginseng production worldwide as it reduces root quality. Full characterization of rusty root is unavailable, and necessary for development of effective control measures. A comparison of phenolics, antioxidants, ginsenosides, and mineral nutrient content of rusted and non-rusted tissue from disease-free roots is reported. Periderm and adjacent tissues of 4-year-old North American ginseng roots ( Panax quinquefolius L.) had a total phenolic content of 3.05 mg·g -1 dry weight (as gallic acid equivalents), which was increased 53% by rust-spotting. Antioxidant activity increased with phenolic content and was 33% higher (3.6 vs. 2.7 mg·g -1 dry weight as ascorbic acid equivalents) in rust-spotted tissue. Total ginsenoside content was higher (139.1 vs. 119.4 mg·g -1 ) in healthy than in rust-spotted tissue, the latter reflecting a significant decrease in four of the major ginsenosides (Rb 2 , Rc, Rd, and Re). The Rg group was higher (38.0 vs. 29.9 mg·g -1 ) in healthy than in rust-spotted tissue. The mineral elements N, P, Ca, Mg, Zn, Mn, and Fe were higher, and K lower (21%) in rust-spotted tissue than in healthy tissue.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.518

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 studies0.0000.001
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 head0.016
GPT teacher head0.240
Teacher spread0.224 · 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

Citations19
Published2003
Admission routes1
Has abstractyes

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