MétaCan
Menu
Back to cohort
Record W2096540891 · doi:10.1002/smll.200500345

Chemical Mapping of Individual Semiconductor Nanostructures

2006· article· en· W2096540891 on OpenAlex
Fulvio Ratto, Andrea Locatelli, Stefano Fontana, Sharmin Kharrazi, Shriwas Ashtaputre, Sulabha K. Kulkarni, Stefan Heun, Federico Rosei

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSmall · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSurface and Thin Film Phenomena
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsAbdus Salam International Centre for Theoretical PhysicsDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsNanoscopic scaleCharacterization (materials science)NanostructureNanotechnologyMaterials scienceSemiconductorStoichiometrySemiconductor nanostructuresSurface (topology)OptoelectronicsChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

We demonstrate experimentally the power of a novel analytical tool for X-ray spectromicroscopy. This provides a minimally intrusive elemental mapping of surfaces at the nanoscale and holds the promise of remarkable versatility. We have applied our procedure to the characterization of Ge(Si) islands on Si(111) substrates, with the aim of investigating the surface stoichiometry gradients and gaining insight into the intermixing dynamics. By identifying Si-richer edges with respect to the centers, we are able to associate alloying in these islands to surface transport processes.

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.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.124
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.015
GPT teacher head0.204
Teacher spread0.189 · 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