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Record W2542395033 · doi:10.5539/jps.v6n1p31

Development of a method to measure mechanical properties of single elongated callose fibers in protoplast cultures of Larix leptolepis and Betula platyphylla

2016· article· en· W2542395033 on OpenAlexfundvenueno aff
Tomoya Oyanagi, Asami Kurita, Toshihiko SHIRAISHI, Hamako Sasamoto

Bibliographic record

VenueJournal of Plant Studies · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSeed Germination and Physiology
Canadian institutionsnot available
FundersUniversity of Calgary
KeywordsBetula platyphyllaCalloseFiberMaterials scienceUltimate tensile strengthCelluloseComposite materialLigninModulusBotanyChemistryCell wallBiology

Abstract

fetched live from OpenAlex

We developed a method to measure mechanical properties of single fibers of callose in liquid protoplast cultures of Larix leptolepis and Betula platyphylla, which were formed in media containing 50 mM of MgCl2 or 100 mM of CaCl2, respectively. Tensile test was performed using two micromanipulators loading micropipettes under an inverted microscope. Spring constant of the pipette used was first calibrated and calculated from using a microbalance. The callose fiber was wired between the two micropipettes. The Young’s modulus of single fibers for Larix and Betula was 7-9 kPa (1.4-1.9 x 104 N/m2) though the diameters of the fiber varied from 10 μm for Larix and 22-26 μm for Betula. No difference was found between experiments with and without medium containing high concentrations of salts. Tensile strength at break was 1.1-1.8 kPa (2.3-3.6 x 103 N/m2). The values are compared to other materials including cellulose containing plant cell wall, cell membranes, and amorphous callose. The value of the Young’s modulus observed was discussed.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.072
GPT teacher head0.286
Teacher spread0.214 · 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
GenreMethods

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

Citations3
Published2016
Admission routes2
Has abstractyes

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