[Comment on “Exaggerated claims about earthquake predictions: Analysis of NASA's method”] Pattern informatics and cellular seismology: A comparison of methods
Bibliographic record
Abstract
The recent article in Eos by Kafka and Ebel [2007] is a criticism of a NASA press release issued on 4 October 2004 describing an earthquake forecast (http://quakesim.jpl.nasa.gov/scorecard.html) based on a pattern informatics (PI) method [Rundle et al., 2002]. This 2002 forecast was a map indicating the probable locations of earthquakes having magnitude m>5.0 that would occur over the period of 1 January 2000 to 31 December 2009. Kafka and Ebel [2007] compare the Rundle et al. [2002] forecast to a retrospective analysis using a cellular seismology (CS) method. Here we analyze the performance of the Rundle et al. [2002] forecast using the first 15 of the m>5.0 earthquakes that occurred in the area covered by the forecasts.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".