Ecological foundations for fire management in North American forest and shrubland ecosystems
Bibliographic record
Abstract
This synthesis provides an ecological foundation for management of the diverse ecosystems and fire regimes of North America based on scientific principles of fire interactions with vegetation, fuels, and biophysical processes. Although a large amount of scientific data on fire exists, most of those data have been collected at small spatial and temporal scales. Thus, it is challenging to develop consistent science-based plans for large spatial and temporal scales where most fire management and planning occurs. Understanding the regional geographic context of fire regimes is critical for developing appropriate and sustainable management strategies and policy. The degree to which human intervention has modified fire frequency, intensity, and severity differs greatly among different ecosystems and must be considered when planning to alter fuel loads or implement restorative treatments. Detailed discussion of six ecosystems—ponderosa pine forest (western North America), chaparral (California), boreal forest (Alaska and Canada), Great Basin sagebrush (intermountain West), pine and pine-hardwood forests (Southern Appalachian Mountains), and longleaf pine (Southeastern United States)— illustrates the complexity of fire regimes and that fire management requires a clear regional focus that recognizes where conflicts might exist between fire hazard reduction and resource needs. In some systems, such as ponderosa pine, treatments are usually compatible with both fuel reduction and resource needs, whereas in others, such as chaparral, the potential exists for conflicts that need to be closely evaluated. Managing fire regimes in a changing climate and social environment requires a strong scientific basis for developing fire management and policy.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".