Bibliographic record
Abstract
The Global Positioning System (GPS) has been widely used to determine the location for a variety of different applications. However, it doesn't work well in indoor environments because it requires the line of sight to the satellites and therefore stops working when the line of sight is not available. High-precision indoor localization is critical to many personal and business applications. After Bluetooth Low Energy (BLE), an energy-efficient version of Bluetooth, is widely deployed, Bluetooth-based indoor localization turns out to be a practical method to locate Bluetooth-enabled devices due to its low battery cost. In this paper, we present two novel BLE-based localization schemes, Low-precision Indoor Localization (LIL) and High-precision Indoor Localization (HIL). Different than most of the existing localization methods that attempt to find the specific location of the object under investigation, LIL and HIL utilize the collected RSSI measurements to generate a small region in which the object is guaranteed to be found. Compared with LIL, HIL leads to smaller localization regions. However, HIL requires an extra data-training phase.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".