MétaCan
Menu
Back to cohort
Record W2094107761 · doi:10.1364/boe.1.001347

Multimodal CARS microscopy of structured carbohydrate biopolymers

2010· article· en· W2094107761 on OpenAlexafffund
Aaron D. Slepkov, Andrew Ridsdale, Adrian F. Pegoraro, Douglas J. Moffatt, Albert Stolow

Bibliographic record

VenueBiomedical Optics Express · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsSteacie Institute for Molecular Sciences
FundersUniversity of Toronto MississaugaUniversity of TorontoIndustrial Technology Research InstituteNational Science Council
KeywordsMicroscopyMaterials scienceOptical microscopeOpticsRaman spectroscopyRaman scatteringCelluloseSecond-harmonic generationStarchCarbohydrateNanotechnologyChemistryScanning electron microscopeComposite materialLaserBiochemistry

Abstract

fetched live from OpenAlex

We demonstrate the utility of multimodal coherent anti-Stokes Raman scattering (CARS) microscopy for the study of structured condensed carbohydrate systems. Simultaneous second-harmonic generation (SHG) and spectrally-scanned CARS microscopy was used to elucidate structure, alignment, and density in cellulose cotton fibers and in starch grains undergoing rapid heat-moisture swelling. Our results suggest that CARS response of the O-H stretch region (3000 cm(-1)-3400 cm(-1)), together with the commonly-measured C-H stretch (2750 cm(-1)-2970 cm(-1)) and SHG provide potentially important structural information and contrast in these materials.

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 score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.006
GPT teacher head0.301
Teacher spread0.295 · 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

Citations47
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueBiomedical Optics ExpressSame topicSpectroscopy Techniques in Biomedical and Chemical ResearchFrench-language works237,207