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Record W2911432454 · doi:10.1126/science.aav5845

Identification of site-specific isotopic labels by vibrational spectroscopy in the electron microscope

2019· article· en· W2911432454 on OpenAlexfundno aff
Jordan A. Hachtel, Jingsong Huang, Ilja Popovs, Santa Jansone‐Popova, Jong K. Keum, Jacek Jakowski, Tracy C. Lovejoy, Niklas Dellby, Ondrej L. Krivanek, Juan Carlos Idrobo

Bibliographic record

VenueScience · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsnot available
FundersOak Ridge National LaboratoryUniversity of AlbertaUniversity of Illinois at ChicagoU.S. Department of Energy
KeywordsElectron microscopeIdentification (biology)SpectroscopyChemistryMaterials scienceAnalytical Chemistry (journal)CrystallographyBiologyPhysicsOpticsChromatography

Abstract

fetched live from OpenAlex

Mapping isotopically labeled alanine Electron microscopy of organic materials must avoid the destructive effects of electron beam impact. One approach is to measure vibrational spectra with electron energy-loss spectroscopy in a mode where the electron beam grazes the sample and couples to it through evanescent modes. Hachtel et al. used such methods to probe carbon-12– and carbon-13–labeled alanine crystals, which exhibited an isotopic shift in the asymmetric carbon-oxygen stretching mode. They used this property to map the distribution of labeled clusters of alanine on length scales of tens of nanometers. Science , this issue p. 525

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.004
GPT teacher head0.248
Teacher spread0.244 · 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

Citations172
Published2019
Admission routes1
Has abstractyes

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