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Record W3129204821 · doi:10.1002/csc2.20467

Pollination system and deficit irrigation affect flavonolignan components of sylimarin, oil, and productivity of milk thistle

2021· article· en· W3129204821 on OpenAlexaff
Mohammad Mahdi Majidi, Fatemeh Pirnajmedin, Ziba Vakili, Samaneh Shahidaval, Nia Hughes

Bibliographic record

VenueCrop Science · 2021
Typearticle
Languageen
FieldMedicine
TopicSilymarin and Mushroom Poisoning
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMilk ThistleSilybum marianumBiologyEcotypePollinationAgronomyThistleOpen pollinationBotanyHorticulturePollen

Abstract

fetched live from OpenAlex

Abstract The biosynthesis and accumulation of secondary metabolites in plant tissues interact strongly with environmental conditions and breeding systems. Limited knowledge is available on the effects of breeding system (self vs. open pollination) and deficit irrigation on the composition of flavonolignans, seed yield, and oil content of different genotypes of milk thistle ( Silybum marianum L.). Four ecotypes of Iranian milk thistle collected from diverse geographical regions were each self‐ and open‐pollinated; they were then assessed in the field under both normal and deficit irrigation for seed yield, oil percentage, silymarin, and its components during 2014 and 2015. Deficit irrigation decreased seed yield and silybin A but increased total silymarin content, silybin B, and silydianin. In both moisture environments, seed yield was positively associated with oil percentage and silybin A. Open pollination improved seed yield, oil percentage, total silymarin, and its components in milk thistle ecotypes as compared with self‐pollination. Considerable genetic variability was observed among the evaluated ecotypes in their response to moisture environments and pollination status. Under open pollination, the northern Iranian ecotypes Mashhad and Sari were identified as promising varieties for further studies.

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 score0.507
Threshold uncertainty score0.212

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 head0.023
GPT teacher head0.275
Teacher spread0.251 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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