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Record W6950614701 · doi:10.5683/sp2/nlb8it

CapnoBase IEEE TBME Respiratory Rate Benchmark

2020· dataset· en· W6950614701 on OpenAlexaff

Bibliographic record

VenueBorealis · 2020
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBenchmark (surveying)PhotoplethysmogramPulse oximetryPulse (music)Waveform

Abstract

fetched live from OpenAlex

The CapnoBase TBME RR benchmark dataset contains 42 cases of 8-min recordings. In addition to the CO2 waveforms (capnograms), these cases have also the Photoplethysmogram (PPG) from pulse oximetry available. Labels from an expert are available for pulse peaks from PPG and breaths from CO2. Also, the benchmark contains the results from the Karlen et al. IEEE TBME paper on multi-parameter RR calculation. This can be used to directly compare algorithm performances. The benchmark dataset is used by researchers to test and compare algorithms. This dataset should not be used to train or tune an algorithm as it may bias the performance results.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0060.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0060.003
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0180.036

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 head0.036
GPT teacher head0.283
Teacher spread0.247 · 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 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

Citations11
Published2020
Admission routes1
Has abstractyes

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