IoT Based Emergency Button for Women Safety
Bibliographic record
Abstract
Abstract: Fundamental to IoT is the instant collaboration that happens between these smart devices. The beauty behind having a network of interconnected devices is that they can all work together to provide real solutions that are much greater than the sum of their parts. IOT based products are always connected and constantly communicating with each other. They regularly exchange information using wired and wireless networks, which helps make our lives easier and safer. When IoT based smart home security systems are used to safeguard our home or possessions, it’s akin to having our favorite reliable friend dutifully watching over our home or pets in our absence. Except it’s actually way better than that, because unlike our well-meaning friend or neighbor, smart home security systems are always present and are ready to instantly alert us of any signs of danger. Smart products, like Nest Lab’s smoke and carbon monoxide alarm, sounds an alert when it detects high levels of CO, and then warns us of the location of the danger. It’s no wonder that Google acquired Nest Labs for a whopping $3.2 billion! Other companies like Canary offer connected smart home security systems that are now a part of a growing trend: using IoT technology to create safety solutions to protect what’s most valuable to us.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.147 | 0.031 |
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".