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Record W4225755258 · doi:10.5376/rgg.2022.13.0004

Analysis of Microarray Data of Rice EPAGRI 108 under Excess Fe Stress

2022· article· en· W4225755258 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueRice Genomics and Genetics · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsnot available
Fundersnot available
KeywordsGeneBiologyMicroarray analysis techniquesGene expressionGermplasmMicroarrayGene ontologyFold changeGene expression profilingGeneticsBotany

Abstract

fetched live from OpenAlex

Iron is one of the essential micronutrients to rice, but the accumulation of excessive ferrous salt in soil can cause toxicity. In this study, bioinformatics method was used to mine the data of Affymetrix rice gene expression microarray to study the differentially expressed genes of rice germplasm EPAGRI 108 under control and excess Fe conditions. The results revealed that 407 genes with more than two fold difference were identified. Compared to the control group, 330 genes were up regulated and 77 genes were down regulated under excess Fe stress. Gene Ontology and pathway analysis revealed that these differentially expressed genes were mainly involved in the biological processes such as oxidoreductase activity, glucose metabolism, amino acid metabolism, etc. Through these data analysis, we preliminarily explored the gene expression patterns of rice under excess Fe conditions, and provided a theoretical basis for further investgating the molecular mechanism tolerating for rice tolerance to Fe toxicity.

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.

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 score0.912
Threshold uncertainty score0.175

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.033
GPT teacher head0.245
Teacher spread0.211 · 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