Bibliographic record
Abstract
Prior to the arrival of humans, fires came and went, the forest grew, burnt, and regrew. Fire was a systematic, natural process within many ecosystems. With the arrival of early humans, fire became more prevalent on the landscape (with the new fire starters), and the ecosystem adapted to the changing regime. It is only in the last 100 years or so that humans have found the need to systematically suppress fire and attempt to eliminate its destructive nature – essentially to tame it. Suppression activities began in earnest after World War II when aeroplanes, helicopters, smokejumpers and new firefighting strategies were introduced. Why have we attempted to remove this element of the ecosystem from its natural role? The answer is simple, of course – fire competes with us for natural resources, fire threatens and destroys our property and it can kill. In many parts of our world, the economy is dependent on the renewable resources found in the forest. Our lives and lifestyles also depend on homes, buildings, telecommunication towers and lines, pipelines and a host of other infrastructure elements in and around wildlands (natural or semi-natural forest) at risk of fire. The normal feeling is that fire cannot be allowed to threaten and disrupt our lives and economy. The existence and mission of fire suppression is thus based on the protection of human life, property and the resources upon which economies depend.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.013 |
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".