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Record W4393842139 · doi:10.5281/zenodo.7945453

In Situ Exfoliation Method of Large-Area 2D Materials

2023· dataset· en· W4393842139 on OpenAlexaff
Antonija Grubišić‐Čabo, Matteo Michiardi, Charlotte E. Sanders, Marco Bianchi, Davide Curcio, Dibya Phuyal, Magnus H. Berntsen, Qinda Guo, Maciej Dendzik

Bibliographic record

VenueUniversity of Groningen research database (University of Groningen / Centre for Information Technology) · 2023
Typedataset
Languageen
FieldEngineering
TopicIndustrial Vision Systems and Defect Detection
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIn situExfoliation jointGeologyMaterials scienceEnvironmental scienceNanotechnologyPhysicsMeteorology

Abstract

fetched live from OpenAlex

Datasets for In Situ Exfoliation Method of Large-Area 2D Materials publication. Extreme UV photoemission band mapping of 2D materials WS2, WSe2 and WTe2 exfoliated in ultra-high vacuum on Au(111), Ag(111) and Ge(001), and AgTe alloy on Ag(111). Measurements were conducted at the BALTAZAR facility in Stockholm, Sweden using a laser-based setup with arepetition rate of 250 kHz, and the fifth harmonic 18.1 eV, and at the SGM-3 beamline of the 3rd-generation ASTRID2 synchrotron radiation source in Aarhus, Denmark with a photon energy 49 eV, 53 eV and 63 eV. All measurements were performed at room temperature.<br> <br> Experimental data are provided in ibw format (Igor Pro).

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.033
GPT teacher head0.285
Teacher spread0.252 · 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 teacher head, 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
Published2023
Admission routes1
Has abstractyes

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