Disaster zone human and animal detection using sonar
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The research introduces a novel approach to animal and human detection using sonar technology in fields such as disaster management, and under water exploration. Unlike traditional visual methods, sonar systems emit soundwaves to analyze echoes, providing unique advantages in challenging environments. The proposed method involves collecting raw sonar data, followed by preprocessing techniques for noise reduction, signal normalization, and feature extraction. Sonar’s ability to penetrate various media, including water and dense fog, makes it valuable for detecting animals and humans in low-visibility conditions. Additionally, sonar operates effectively in both day and night settings, unaffected by lighting conditions. The proposed detection system will undergo comprehensive experiments using representative datasets and real-world scenarios. Performance metrics, such as detection accuracy, precision, recall, and computational efficiency, will be analyzed and compared with existing approaches. The study showcases the effectiveness and viability of employing sonar technology for animal and human detection tasks, highlighting its unique capabilities in challenging environments.
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.
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 | 0.000 | 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 | 0.000 | 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 it