Bibliographic record
Abstract
This paper is an expansion of the thoughts I presented in the closing plenary at the 4 th International Fire Ecology and Management Conference in Savannah, Georgia, USA. After ruminating over several days of oral presentations and posters and chatting with attendees, I concluded: 1) scientists are still wrestling with the same fundamental problems they have been for decades, 2) managers are increasingly skeptical of the proliferation of models because they don’t provide reliable predictions in a timely fashion, and 3) competitors for airspace in which to release combustion products have become much more adept at convincing regulators to tighten the screws on prescribed fire instead of on their industries. Yet the general mood of the attendees and overall conference atmosphere was highly positive. Perhaps this was because the attendees agree with me that healthy ecosystems are the key to our long-term survival on planet Earth—a planet that has been shaped by fire for millennia and that continues to require periodic fire to maintain healthy ecosystems, thus making prescribed fire the “Ecological Imperative.” Because fire managers have the high ground, I continue to be optimistic that, if we can stifle our self-serving tendencies, be factual, and not exaggerate the benefits nor gloss over the deleterious ramifications of prescribed fire, we can educate the general public and turn them into vocal advocates for the judicious use of fire. My primary objective in this paper is to share some concepts that guided me throughout my career with the hope that they will motivate you to improve your modus operandi and inspire you to expand your fire management outreach activities.
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 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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.062 | 0.075 |
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".