Use of Neural network to predict the peak ground accelerations and pseudo spectral accelerations for Mexican Inslab and Interplate Earthquakes
Why this work is in the frame
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Bibliographic record
Abstract
The use of Artificial Neural Networks (ANN) is explored to predict peak ground accelerations (PGA) and pseudospectral acceleration (SA) for Mexican inslab and interplate earthquakes. A total of 277 and 418 seismic records with two horizontal components for inslab and interplate earthquakes, respectively, are used to train the ANN models by using an ANN with a feed-forward architecture with a back-propagation learning algorithm. Both ANN with single and two hidden layers are considered. For comparison purposes, the PGA and SA values predicted by the trained ANN models are compared with those estimated with attenuation relations or ground motion prediction equations (GMPEs). The comparison indicates that the predicted PGA and SA values by the trained ANN models, in general, follow the trends predicted by the GMPEs. However, an extensive verification of the trained models must be conducted before they can be used for seismic hazard and risk analysis since, on occasion, the PGA and SA values predicted by the trained ANN models depart from the behaviour observed from the actual records.doi: https://doi.org/10.1016/S0016-7169(14)71489-8
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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