[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 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 | 0.020 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.026 | 0.021 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".