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Record W7117537149 · doi:10.5683/sp3/brluse

ECG and RR Interval Data from 12 Longest Dives in Steller Sea Lions

2025· dataset· W7117537149 on OpenAlexaffabout
Rhea L. Storlund

Bibliographic record

VenueBorealis · 2025
Typedataset
Language
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSea lionRR intervalInterval (graph theory)Heart rateRaw data

Abstract

fetched live from OpenAlex

This dataset contains cardiac data collected from three Steller sea lions (Eumetopias jubatus) during trained, stationary breath-hold dives to depths of 3–4 m, conducted in a pool at the Vancouver Aquarium. Heart rate data were recorded using subcutaneous cardiac monitors (Medtronic Reveal XT) as part of a study investigating cardiovascular control and heart rate regulation during trained diving. The dataset includes CSV files containing extracted and manually corrected RR intervals with hand-coded annotations, as well as ECG traces exported as PDF files from the Medtronic Reveal XT system. These data support the analyses reported in the associated publication on heart rate dynamics and oscillatory patterns during dives in marine mammals. Both raw and processed cardiac data are provided to support transparency, reproducibility, and potential reanalysis of heart rate and dive response physiology.

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)0.001
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 score0.022
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.010

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 head0.063
GPT teacher head0.321
Teacher spread0.258 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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