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Record W2104921836 · doi:10.5539/jmsr.v5n1p46

Comparative Relationship of Fiber Strength and Yarn Tenacity in Four Cotton Cultivars

2015· article· en· W2104921836 on OpenAlexvenueno aff
Yongliang Liu, B. Todd Campbell, Chris Delhom, Vikki Martin

Bibliographic record

VenueJournal of Materials Science Research · 2015
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsnot available
FundersU.S. Department of Agriculture
KeywordsTenacity (mineralogy)YarnCultivarFiberComposite materialMathematicsMaterials scienceAgronomyBiology

Abstract

fetched live from OpenAlex

<p class="1Body">High volume instrumentation (HVI<sup>TM</sup>) measurement is a primary and routine tool of providing fiber properties to cotton researchers. There have been considerable studies designed to derive yarn quality from acquired fiber quality data by various means, including HVI. There is also of desired information about the comparison of yarn quality within a cotton cultivar or among the cultivars, as such knowledge could be informative in attempts to understand the selection of cotton cultivars. The purpose of this preliminary study was to characterize the fiber HVI strength and yarn skein tenacity of four cotton cultivar harvested from three locations in different crop years. Instead of developing linear regression models from acquired fiber property parameters to predict yarn tenacity, this study applied a simple ratio method (i.e., correct fiber strength or yarn tenacity with fiber micronaire component) to relate fiber strength with yarn tenacity. The results indicate that three cultivars (DP 393, Phytogen 72, and FM 958) show stronger correlation between micronaire corrected yarn tenacity and micronaire corrected fiber HVI strength. It implies the feasibility of utilizing HVI fiber micronaire and strength property data, as a semi-quantitative and fast tool, to compare the yarn tenacity performance within a cotton cultivar or between cultivars.</p>

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)0.023
metaresearch head score (Gemma)0.003
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 score0.103
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.336
GPT teacher head0.466
Teacher spread0.130 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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