Untangling the Prometheus Nightmare
Bibliographic record
Abstract
A number of bush and forest fire simulators have been developed in the last two decades based on the deterministic spread equations of Gwynfor Richards (Int. J. Num. Meth. Eng. 1990) and the marker method for computing discrete approximations to the Richards equations. 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, lead to tangling and other non-physical singularities. 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 which 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 untangling in particular. We describe the mathematical foundations for our new method and give a brief overview of the programming issues that arise in implementation. Finally, we report the results obtained by the new model on a test database of fires used by Prometheus developers. Performance is assessed both in terms of run times and in the ability of the code to produce realistic fire fronts without operator intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".