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Record W3043641424 · doi:10.1139/cjps-2019-0314

Absorption and translocation of dicamba in dicamba-tolerant wild tomato

2020· article· en· W3043641424 on OpenAlexvenueno aff
Rouzbeh Zangoueinejad, Mohammad Taghi Alebrahim, Te‐Ming Tseng

Bibliographic record

VenueCanadian Journal of Plant Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsnot available
FundersNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsDicambaTrichomeChromosomal translocationBiologyCultivarHorticultureSolanumBotanyAgronomyWeed controlGenetics

Abstract

fetched live from OpenAlex

Herbicide tolerance is commonly associated with reduced absorption and translocation of the herbicide; we hypothesized that the mechanism of dicamba tolerance in wild tomato (Solanum lycopersicum L.) accessions is due to these characteristics. The absorption and translocation of dicamba were investigated at a drift rate of 2.8 g a.e. ha −1 in three predetermined dicamba-tolerant (DT) wild accessions (TOM199, TOM198, and TOM300) and compared with two dicamba-susceptible (DS) commercial tomato cultivars [Money Maker (MM) and Better Boy (BB)]. Dicamba was quantified in three different parts of the tomato plant: two upper leaves, two lower leaves, and the roots at 1, 3, and 7 d after treatment. Both MM and BB absorbed more dicamba then all the three DT accessions. The overall translocation pattern of dicamba was similar between DS cultivars and DT accessions, thus suggesting that tolerance to dicamba in wild accessions may not be associated with reduced translocation but instead with reduced uptake of the herbicide. Additionally, reduced dicamba absorption in DT accessions may be attributed to their leaf characteristics, such as the presence of narrower leaves (3.42 leaf length/width ratio) and higher trichome density (20 no. mm −2 ) in DT accessions, than compared with DS cultivars (1.92 leaf length/width ratio and 8 no. mm −2 trichome density).

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.981
Threshold uncertainty score0.998

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 head0.016
GPT teacher head0.189
Teacher spread0.173 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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