Reply to "Comment on 'Three Theorems of Earthquake Location' by Cinna Lomnitz" by Catherine Woodgold
Bibliographic record
Abstract
Here is the gist of my three “theorems” (I hope the self-irony is appreciated). Suppose that we wish to locate Ottawa. A message is sent out from a point somewhere deep beneath Ottawa. It arrives first in New York and then in Moscow. What can we say about this observation? Let us bisect the arc New York–Moscow. We may state confidently that Ottawa is in the hemisphere that contains New York, not in the one that contains Moscow. How so? Because the great circle that bisects the arc New York–Moscow is the locus of all points that are equidistant from New York and Moscow. If some more observations are provided—Beijing, say, and Brisbane—we may repeat the operation sequentially as many times as there are pairs of observations. Theorem 1 says that Ottawa is contained in the intersection of all hemispheres that also contain the earliest of any pair of stations. This intersection is a topology; in fact, it is the smallest closed set containing Ottawa and can be made still smaller by adding more observations. But (here is the point of the exercise) we cannot determine the depth of the source located directly beneath Ottawa. The focal depth remains unconstrained. In functional analysis, we call such problems ill …
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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 | 0.009 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.033 | 0.065 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".