Correction to “Critical point theory of earthquakes: Observation of correlated and cooperative behavior on earthquake fault systems”
Bibliographic record
Abstract
[1] In the paper “Critical point theory of earthquakes: Observation of correlated and cooperative behavior on earthquake fault systems” by C. Chen et al. (Geophysical Research Letters, 33, L18302, doi:10.1029/2006GL027323, 2006), for the PI analysis of the 1995 Kobe, Japan, earthquake, we used the earthquake catalogue provided to us by B. Enescu at the Disaster Prevention Research Institute (DPRI) in Kyoto University, Japan. The Japanese earthquake data set originates from both the DPRI and the Japanese Meteorological Agency (JMA), and these sources should have been acknowledged in our paper with great thanks for permission to use these data. We deeply regret this important omission. Meanwhile, the authors also would like to acknowledge the important conversations with B. Enescu that significantly clarified our understanding of the precursory patterns for the Kobe earthquake. The large area of PI anomalies presented in our paper is in accord with the result presented by Enescu and Ito [2001]. The complex seismic anomalies before the Kobe earthquake are manifested in a rather large area, which corresponds with the preparation zone of the Kobe earthquake. The analysis presented by Enescu and Ito [2001] revealed the quiescence and activation phases before the Kobe earthquake as well.
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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 | 0.006 | 0.077 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.028 | 0.020 |
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".