On simulating episodic events against a background of noise-like non-episodic events
Bibliographic record
Abstract
Simulation, as an art and a science, deals with the issue of allowing the practitioner to model events using their respective probability distributions. Thus, it is customary for simulations to model the behaviour of accidents, telephone calls, network failures etc. In this paper, we consider a relatively new field, namely that of modelling episodic events such as earthquakes, nuclear explosions etc. The difficulty with such a modelling process is that most of the observations appear as noise. However, when the episodic event does occur, its magnitude and features far overshadow the background, as one observes after a seismic event. In this paper, we demonstrate how the effect of a particular form of episodic event can be modelled as it propagates through the underlying background noise. Furthermore, we illustrate how the subsequent decay of the event can also be modelled and simulated. In demonstrating this concept, we utilize the exemplar scenario posed by the Comprehensive Nuclear-Test-Ban Treaty (CTBT), and model the propagation and decay of radionuclides, emitted from clandestine, subterranean nuclear detonations, through the background levels resulting from the global nuclear industry.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".