Bibliographic record
Abstract
When considering forest fires and the survival of plants and animals there is a glaring paradoxical imbalance. A flame has a temperature of around 800–1200 °C whether it is the gentle flame of a match or candle, or a raging forest fire (see Chapter 3 for a longer discussion), and physiologically active living tissue (plant or animal) is killed by a short exposure to temperatures above 50–60 °C. A burning forest does not consist of solid flame, so inevitably the temperature inside the forest is not uniformly that of a burning flame. Hot air rises, dragging in colder air near the ground from the sides. Thus although the centre and top of a burning canopy may be above 1000 °C, temperatures may be expected to reduce with height to perhaps just a few hundred degrees, unless there is a marked radiation of heat downwards from burning fuel above ground level (such as in dense shrubbery). Even so, temperatures above 100 °C may persist for up to several minutes near the ground, especially if the ‘burnout time’ is long (see Box 5.1). The main key to surviving forest fires is to keep the heat of the flame away from living tissue. How this is done very much depends on whether you are considering plants or animals, and if looking at plants, what sort of fire is burning – whether it is a ground fire, a surface fire or a crown fire. Some plants will also continue via seeds if the parent is killed.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".