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Record W2096477954 · doi:10.1139/b07-006

Isotopic fractionation of zinc in field grown tomato

2007· article· en· W2096477954 on OpenAlexvenueno aff
Timothy R. Cavagnaro, Louise E. Jackson

Bibliographic record

VenueCanadian Journal of Botany · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsnot available
FundersUniversity of Adelaide
KeywordsFractionationShootZincIsotopes of zincIsotopeStable isotope ratioIsotope fractionationChemistrySolanumBotanyBiologyHorticultureEnvironmental chemistryChromatography

Abstract

fetched live from OpenAlex

Many of the world’s soils are deficient in zinc (Zn), and this has implications for plant and human nutrition. Consequently, there is a need to better understand plant uptake and allocation of Zn. Natural abundances of stable isotopes have been used to gain insight into uptake, assimilation, and allocation of various elements by plants. Inductively coupled plasma mass spectrometry was used to study the fractionation of Zn isotopes in the shoots and fruits of mature tomato plants (Solanum lycopersicum L.) grown on an organic farm. Effects of mycorrhizal colonization of roots on Zn fractionation were studied by growing a tomato mutant with reduced mycorrhizal colonization, and its mycorrhizal wild-type progenitor. Fruits of both genotypes were enriched in 64 Zn and 66 Zn and depleted in 67 Zn and 68 Zn isotopes, based on calculations that expressed the concentration of each isotope as a percentage of total Zn. The reverse was true of the shoots. Furthermore, shoots of the mycorrhizal genotype were very slightly enriched in 64 Zn and 66 Zn isotopes relative to those of the reduced mycorrhizal colonization genotype. Possible explanations for fractionation of Zn between shoots and fruits, including differential bonding of Zn to cellular components, processes affecting Zn–phytate–protein complexes, and Zn transport and translocation processes are discussed.

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 score0.551
Threshold uncertainty score0.879

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 head0.009
GPT teacher head0.209
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
Published2007
Admission routes1
Has abstractyes

Explore more

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