Identifying Climate Anomalies with Simulated Antenna Data, Sensor Arrays, and Spiking Neural Networks
Bibliographic record
Abstract
This paper proposes a novel framework for climate anomaly detection by integrating antenna array-derived environmental data with Spiking Neural Networks (SNNs), a biologically inspired computational approach. The methodology systematically captures key climatic parameters, including temperature, wind, and moisture, allowing a precise and structured analysis. By encoding temporal and spatial dynamics, SNNs provide an advanced mechanism for detecting subtle patterns and anomalies that conventional methods may overlook. The study demonstrates the effectiveness of simulated antenna-based climate data in identifying temperature trend anomalies, reinforcing the potential of neural architectures in climate variability analysis. The results highlight the ability of SNNs to improve anomaly detection through efficient processing of time-sensitive and spatially complex datasets. The proposed pipeline not only addresses methodological gaps but also improves the broader understanding of climate disturbances by incorporating innovative technology-driven solutions. This research underscores the importance of integrating computational intelligence into climate studies, contributing to more accurate and scalable environmental monitoring systems.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".