Bibliographic record
Abstract
ABSTRACT In the O’Doherty-Anstey model, the spectrum of a seismic wave transiting and reverberating within a stack of interfaces is estimated in terms of its reflectivity. At the center of the model is a discrete combinatorical calculation, in which the contributing raypaths at each lag are weighted and counted. The model maps naturally into an analysis based on statistical mechanics, with raypaths playing the role of system configurations and lag playing the role of system energy. This leads to an expression for the probability of finding a contribution to the wave at a given lag, determined up to a partition function and a scattering parameter analogous to temperature. The partition function is estimated within the O’Doherty-Anstey model itself, and the scattering parameter can be adjusted to describe geologic media with increased or decreased scattering potential. The average contributing lag deriving from this analysis exhibits a continuous phase transition, separating two distinct scattering regimes, one in which the direct wave dominates and the other in which the scattering dominates. The weighting produced by the reflectivity appears to govern the smoothness of the transition.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".