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Record W2290687549 · doi:10.5539/jas.v8n2p46

Production of New Cotton Interspecific Hybrids with Enhanced Fiber Fineness

2016· article· en· W2290687549 on OpenAlexvenueno aff
Nacoulima Lalaissa Nafissatou, Diouf Fatimata Hassedine, Konan N’guessan Olivier, Mergeai Guy

Bibliographic record

VenueJournal of Agricultural Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicResearch in Cotton Cultivation
Canadian institutionsnot available
Fundersnot available
KeywordsHybridGossypium hirsutumBackcrossingBiologyGossypiumCultivarHorticultureFinenessBotanyChemistryGeneticsGene

Abstract

fetched live from OpenAlex

To improve cotton fiber fineness, the (Gossypium hirsutum L. × Gossypium longicalyx Hutch. & Lee)² allohexaploid and the [(Gossypium hirsutum L. × Gossypium thurberi Tod.)² × G. longicalyx] allotetraploid were backcrossed to G. hirsutum to produce introgressed genetic stocks. The ribbon width (RW) of 600 swelled fibers produced by the hybrids, their parents, and their backcross progeny were analyzed for each compared genotype using an optical microscope. The RWs varied between 6.41±2.15 µm for G. longicalyx to 17.45±2.98 µm for the G. hirsutum parent cultivar C2. Fibers produced by the trispecific hybrids and their progeny were finer than the bispecific hybrid material. For the introgressed stocks, the lowest RWs were observed for the trispecific hybrid (10.79±2.14 µm) and certain backcross progenies (between 11.98±1.27 µm to 12.71±1.61 µm). The allohexaploid RW was 13.58±1.41 µm. One of its tetraploid progeny produced approximately the same value (13.94±2.48 µm). These results show that G. longicalyx is a potential genetic stock for cotton fiber fineness improvement. The genetic stocks produced are valuable materials for improve the fineness of cotton fiber.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.030
GPT teacher head0.261
Teacher spread0.231 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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