FORESTS, FIRES and STOCHASTIC MODELLING John Braun (University of Western Ontario),
Bibliographic record
Abstract
Statisticians have an important role to play in the study of various aspects of forestry. The objective of the BIRS workshop was to facilitate interaction between statisticians and researchers that study forest fires and forest ecology. One theme that emerged in several of the keynote talks as well as in the roundtable discussions was the importance of melding science with statistics. In some of the talks, physically reasonable differential equation models as well as other types of deterministic models were augmented to incorporate the natural variability inherent in some of the systems observed (e.g. animal trajectories, weather, fire behaviour). It is likely that advances in forestry science and statistics will be made rapidly, if these types of approaches are emulated in other situations. As this report will indicate, the outcome of this meeting was enhanced collaboration among these groups of researchers and an increase in energy and enthusiasm to solve open forestry-related statistical problems. This report begins with a brief overview of the field (forest fire and forest ecology research). The next section gives summaries of the main presentations. The subsequent section highlights some of the progress made during the workshop; summaries of four roundtable discussions are contained there. This report con-cludes with an outline of the collaborations that are emerging as a result of this highly inter-disciplinary workshop. A brief bibliography is given at the end; a more extensive bibliography can be obtained from
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".