Long-term (2004-2022) analysis of TIR anomalies over Turkish area: the case of Kahramanmaraş (February 6th 2023, M7,8) EQ
Bibliographic record
Abstract
A long-term (2004-2015) analysis of the fluctuations of Earth’s thermally emitted radiation, measured by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (MSG) satellite in the thermal infrared (TIR) spectral range (i.e. 10-12 µm), have been performed in order to investigate the complex process of preparation of earthquakes in that area. Such analysis showed that more than 67% of all identified (space-time persistent) anomalies occur in the pre-fixed space-time window around the occurrence time and location of earthquakes (M≥4), with a false positive rate smaller than 33%. Moreover, Molchan error diagram analysis gave a clear indication of non-casualty of such a correlation, in comparison with the random guess function. In this paper, the characterization of the signal, in terms of punctual expected values and related variability, have been extended up to 2022 and the Robust Satellite Technique (RST) and RETIRA (Robust Estimator of TIR Anomalies) index applied to identify possible Significant Sequences of TIR Anomalies (SSTAs) in 2023 in relation with the seismic sequence that hit the region of Kahramanmaraş starting with the February 6th, M7,8 EQ.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".