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Record W3046277939 · doi:10.1017/s1431927620019431

NanoMi: An Open Source (Scanning) Transmission Electron Microscope.

2020· article· en· W3046277939 on OpenAlexaff
Marek Malac, Martin Cloutier, Mark Salomons, Sean Chen, Suliat Yakubu, Marcus Leeson, Jason Pitters, D. Vick, Drew Price, Darren Homeniuk, Misa Hayashida, R.F. Egerton

Bibliographic record

VenueMicroscopy and Microanalysis · 2020
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsNational Research Council CanadaUniversity of British ColumbiaNational Institute for NanotechnologyUniversity of Alberta
Fundersnot available
KeywordsScanning transmission electron microscopyMaterials scienceConventional transmission electron microscopeScanning electron microscopeTransmission electron microscopyOptoelectronicsNanotechnologyComposite material

Abstract

fetched live from OpenAlex

Marek Malac, Martin Cloutier, Mark Salomons, Sean Chen, Suliat Yakubu, Marcus Leeson, Jason Pitters, Doug Vick, Drew Price, Darren Homeniuk, Misa Hayashida

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.994
Threshold uncertainty score0.357

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.002
Science and technology studies0.0020.001
Scholarly communication0.0020.005
Open science0.0060.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.1070.048

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 head0.013
GPT teacher head0.295
Teacher spread0.281 · 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 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

Citations5
Published2020
Admission routes1
Has abstractyes

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