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Record W2170342373 · doi:10.5539/jas.v2n2p62

Mapping QTLs Related to Plant Height and Root Development of Eragrostis tef under Drought

2010· article· en· W2170342373 on OpenAlexvenueno aff
Hewan Demissie Degu, Tatsuhito Fujimura

Bibliographic record

VenueJournal of Agricultural Science · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsnot available
Fundersnot available
KeywordsEragrostisBiologyShootQuantitative trait locusDrought tolerancePopulationAgronomyWater stressDrought stressHorticultureGene

Abstract

fetched live from OpenAlex

The effects of water-stress on root and shoot growth of tef (Eragrostis tef) was evaluated with a population of94 recombinant inbred lines (RILs) derived from a cross between tef (cv. Kaye Murri) and E. pilosa. The youngseedlings were cultured under well-watered (soil water potential; 0.2 MPa) and water-stressed (-1.6 MPa)conditions, and plant height and primary root length were measured after 15 days of culture. Kaye Murriconsistently showed larger plant height and longer primary roots than E. pilosa under drought. Quantitative traitloci (QTLs) were also mapped in relation to water-stress using traits of RILs. Five and seven QTLs for plantheight and nine and eight QTLs for primary root length under both of well-watered and water-stressed conditionswere identified, respectively. Seven and six QTLs for indexes of response to plant height and primary root lengthwere also found, respectively. Phenotypic variations for a single QTLs was in the range of 2% to 20%. Beside ofKaye Murri, E. pilosa provided promotive QTLs for development of shoots and roots under drought, indicatingthis species is also an important genetic resource for the breeding of tef.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2010
Admission routes1
Has abstractyes

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Same venueJournal of Agricultural ScienceSame topicGenetic Mapping and Diversity in Plants and AnimalsFrench-language works237,207