Accuracy and Precision of the SlateSafety BandV2 and CORE Devices in Estimating Resting and Moderate Hyperthermic Exercise Temperature in Eumenorrheic Females
Bibliographic record
Abstract
PURPOSE: Core temperature (Tcore) monitoring is used in the prevention of heat illnesses and for heat-acclimation purposes. We examined the accuracy and precision of 2 commercially available devices (BandV2 and CORE) that estimate Tcore versus rectal temperature. METHOD: Eight eumenorrheic females (V˙O2max: ∼41 mL·kg-1·min-1) completed 60 minutes of cycling in the follicular phase and the luteal phase over 2 separate cycles, wearing a minimally permeable clothing ensemble to amplify thermal load. RESULTS: Both devices proved to be precise at rest and during exercise. Between duplicate follicular and luteal tests, the CORE device bias was 0.1400 (0.33) °C and 0.0331 (0.42) °C, and the BandV2 device bias was 0.0418 (0.18) °C and -0.0171 (0.21) °C. Compared with rectal temperature, accuracy was below our preestablished criterion of ±0.27 °C. At rest, the devices underestimated Tcore: BandV2, -0.2735 (0.25) °C, and CORE, -0.2746 (0.28) °C, and at the 55-minute time point, both devices overestimated Tcore: BandV2, +0.5117 (0.37) °C, and CORE, +0.3319 (0.43) °C. The delta increase in Tcore did not differ between menstrual-cycle phases. CONCLUSIONS: The BandV2 and CORE indirect sensors currently offer precise but not accurate estimates of Tcore.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".