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Record W4231596905 · doi:10.1063/1.4712286.1

10.1063/1.4712286.1

2012· dataset· en· W4231596905 on OpenAlexaff
Aleksander Labuda, Kei Kobayashi, Yoichi Miyahara, Peter Grütter

Bibliographic record

VenueDefault Digital Object Group · 2012
Typedataset
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsCantileverExcitationAtomic force microscopySpectroscopyForce spectroscopyInterpretation (philosophy)Materials sciencePhotothermal therapyNanotechnologyOpticsPhysicsComputer scienceComposite materialQuantum mechanics

Abstract

fetched live from OpenAlex

It is well known that the low-Q regime in dynamic atomic force microscopy is afflicted by instrumental artifacts (known as “the forest of peaks”) caused by piezoacoustic excitation of the cantilever. In this article, we unveil additional issues associated with piezoacoustic excitation that become apparent and problematic at low Q values. We present the design of a photothermal excitation system that resolves these issues, and demonstrate its performance on force spectroscopy at the interface of gold and an ionic liquid with an overdamped cantilever (Q < 0.5). Finally, challenges in the interpretation of low-Q dynamic AFM measurements are 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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.755
Threshold uncertainty score0.818

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.007
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.2450.511

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 head0.008
GPT teacher head0.262
Teacher spread0.254 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2012
Admission routes1
Has abstractyes

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