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Record W4321351229 · doi:10.1080/15592324.2023.2180159

Effect of light conditions on trophic level and gene expression of partially mycoheterotrophic orchid, <i>Cymbidium goeringii</i>

2023· article· en· W4321351229 on OpenAlexfundno aff
Keita Chagi, Hiroaki Komoda, Masashi Murakami

Bibliographic record

VenuePlant Signaling & Behavior · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
FundersInstitute of Genetics
KeywordsBiologyMixotrophJasmonic acidAutotrophTrophic levelBotanySymbiosisNutrientTranscriptomeCymbidiumShadingPhenotypic plasticityPhotosynthesisGeneGene expressionHeterotrophEcologyGeneticsBacteria

Abstract

fetched live from OpenAlex

Partial mycoheterotrophic i.e., mixotrophic, plants are the species which partially depend on mycorrhizal fungi for its nutrients. Although some of these plants are known to show plasticity in the degree of fungal dependence induced by the changes in light condition, the genetic background of this plasticity is largely unsolved. Here, we investigated the relationships between environmental conditions and nutrient sources based on 13C and 15N enrichment in mixotrophic orchid Cymbidium goeringii. We also shaded them for 2 months and evaluated the effect of light condition on the nutrient sources based on the abundance of 13C and 15N and the gene expressions by RNA-seq based de novo assembly. The shading had no effect on isotope enrichment, possibly because of the translocation of carbon and nitrogen from the storage organs. Gene expression analysis showed the upregulation of genes involved in jasmonic acid response in leaves of the shaded plants, which suggests that the jasmonic acid played an important role in regulation of degree of dependence against the mycorrhizal fungi. Our results suggest that mixotrophic plants might be controlling their dependency against the mycorrhizal fungi by a common mechanism with the autotrophic plants.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.244

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 head0.054
GPT teacher head0.247
Teacher spread0.193 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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