Untangling the Prometheus Nightmare
Bibliographic record
Abstract
Abstract: Numerous bush and forest fire simulators have been developed in the last two decades based on elliptical spread and Huygens ’ principle. Unfortunately, all such implementations are plagued by topological complications. For example, sampling issues on the evolving front, represented in the Canadian simulator Prometheus as a set of polygons, evolving under the differential equations derived by Gwynfor Richards from Huygens ’ principle, lead to tangling and other non-physical singularities. In order to maintain stability of the numerical scheme, and to produce realistic fire fronts, these artefacts must be systematically removed between time steps. In the literature on interface tracking, this is called delooping or untangling the computed front. Recently, an automated untangling routine has been developed for Prometheus based on the so-called 2-colour Theorem. Not only is this approach more intuitive than previous algorithms (which were based on scan lines and winding number calculations), it has proved to be more accurate and faster on all test cases employed by Prometheus developers (from 10 to 90 percent speedup compared to previous generation codes, depending on the examples). It is based on a concise and easy-to-implement set of rules that do not introduce the many special cases that previous methods required. This report presents a brief review of fire simulation models in general, and on their various approaches to
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".