Bibliographic record
Abstract
The dataset provides a comprehensive overview of various health and activity metrics tracked by Garmin devices over a specified monitoring interval. Each entry is uniquely identified by a distinct userId, facilitating individualized analysis and comparison across users. The temporal aspect of the data is captured through the calendarDate feature, denoting the date and time of each recorded observation in the format of mm/dd/yyyy. This temporal information enables temporal analysis and trend identification over the monitoring period. Activity levels are quantified through metrics such as steps and distanceInMeters, shedding light on users’ physical movement patterns and total distance traveled. Concurrently, the activeTime metric offers insights into the duration of active engagement, vital for assessing users’ overall activity levels and adherence to recommended physical activity guidelines.Moreover, the dataset includes essential physiological indicators such as heart rate and stress levels, crucial for understanding users’ overall health and well-being. Metrics like averageHeartRate and maxHeartRate provide insights into users’ cardiovascular health and exertion levels during the monitoring period. Meanwhile, stress-related metrics such as averageStressLevel and stressDurationInSeconds offer valuable information regarding users’ stress levels and the duration spent in various stress states. Additionally, sleep-related metrics such as sleepDurationInSeconds and deepSleepDuration provide insights into users’ sleep patterns and quality, essential for assessing overall sleep health and identifying potential sleep disorders. Overall, this dataset presents a rich source of information for researchers and practitioners interested in understanding users’ activity levels, physiological responses, and sleep patterns in the context of health monitoring and intervention strategies.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.017 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".