Aspects of Body Metrics Data Management in the Long Term for the European Fitness Industry
Bibliographic record
Abstract
Abstract The dawn of the fourth industrial revolution, mostly known through the German initiative “Industrie 4.0”, builds on a set of technologies emerging from software and information and communication technologies (ICT); paired with the growth of the Internet-of-Things (IoT), the so-called “smart products” are expanding on the market. These smart products integrate data collection and processing capacities. Additionally, the collected data have their own lifecycle, and can be classified as sensitive data. In that sense, companies developing hardware products may need support to step in “smart products” development. Digital transformation strategy is a possible overall support for companies. However, regarding smart product development and IoT data management, no studies to date have addressed formalized guidelines to support companies. This article proposes a set of guidelines focusing on IoT data management to support hardware companies in their transformation towards smart products. The proposed guidelines are exemplified on a fitness industry case which is using wearable devices collecting body metrics, considered as sensitive data.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".