Bibliographic record
Abstract
Major forest types that are characterized by nonlethal understory fire regimes include those where ponderosa pine or Jeffrey pine has been a major component either as a fire-maintained seral type or as the self-perpetuating climax (table 5-1). This includes extensive areas throughout the Western United States from northern Mexico to southern British Columbia, Canada (Little 1971). Also, sizeable areas of open woodlands dominated by Oregon white oak, California black oak, blue oak, or Digger pine were characterized by frequent understory fires largely due to deliberate burning by Native Americans (Boyd 1986; Lewis 1973). These occurred in relatively dry areas west of the Cascades and Sierra Nevada from the southwest corner of British Columbia to southern California. Recent studies suggest that large areas of the redwood forest in coastal northern California were characterized by frequent understory fires resulting from burning by Native Americans (Brown and Swetnam 1994; Duncan 1992; Finney and Martin 1989; Greenlee and Langenheim 1990). Additionally, portions of other forest types may also have had understory fire regimes. For example, some areas of interior Douglas-fir near the drought-caused lower timberline in the higher valleys of the Rocky Mountains may have been maintained in open condition in understory fire regimes (Arno and Gruell 1983; Arno and Hammerly 1984). Nevertheless, most of this type is best represented by the mixed regime.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".