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Record W7084399273 · doi:10.5683/sp3/xv8vfw

In situ measurement of the specific surface area of reduced graphene oxide using time-resolved laser induced incandescence

2025· dataset· en· W7084399273 on OpenAlexafffund

Bibliographic record

VenueBorealis · 2025
Typedataset
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Technological Innovation
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIncandescent light bulbIncandescenceWavelengthOxideLaserIn situGraphene

Abstract

fetched live from OpenAlex

The data is recorded from a laser-induced incandescent measurement of reduced graphene oxide reduced at different reduction temperatures using a tube furnace. Intensity data of incandescent traces recorded by four separate PMTs at wavelengths of 447 nm, 545 nm, 645 nm, and 747 nm. The data is processed into five different files, named based on the reduction temperature of the tube furnace. Each column represents a different wavelength range, as follows: 1: 447 nm, 2: 747 nm, 3: 545 nm, 4: 645 nm. The data is relatively calibrated and shortened to reflect the temperature data from the peak of the signal. Additionally, time data is appended, which directly correlates to the time of the incandescent signal.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.017
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.024

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 head0.082
GPT teacher head0.240
Teacher spread0.158 · 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
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
Published2025
Admission routes2
Has abstractyes

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