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Record W1777066175 · doi:10.5376/pgt.2010.01.0001

TraitMill: a Discovery Engine for Identifying Yield-enhancement Genes in Cereals

2010· article· en· W1777066175 on OpenAlexvenueno aff
Christophe Reuzeau, J. Pen, Valérié Frankard, Joris De Wolf, R. Peerbolte, Willem F. Broekaert, Wim Van Camp

Bibliographic record

VenuePlant Gene and Trait · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsnot available
Fundersnot available
KeywordsYield (engineering)GeneBiologyBiotechnologyComputational biologyGeneticsMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Transgenesis is a powerful and effective mode to study plant development. CropDesign has developed the TraitMill platform, a high-throughput technology that enables large-scale transgenesis and plant evaluation. The TraitMill is a highly versatile tool for testing the effect of genes and gene combinations on plant phenotypes. It can be used to successfully evaluate hundreds of independent promoter-genes combinations per year, either under optimal growth conditions or under different abiotic or nutrient stress regimes. The TraitMill platform operates in rice and is specially designed to measure alterations in growth with high sensitivity. To date TraitMill is the only platform that combines these two features and is therefore uniquely placed to identify genes that improve the yield of cereals

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 score0.525
Threshold uncertainty score0.203

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 head0.041
GPT teacher head0.232
Teacher spread0.191 · 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

Citations59
Published2010
Admission routes1
Has abstractyes

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