The influences of drought and humans on the fire regimes of northern Pennsylvania, USA
Bibliographic record
Abstract
Understanding past fire regimes is necessary to justify and implement restoration of disturbance-associated forests via prescribed fire programs. In eastern North America, the characteristics of many presettlement fire regimes are unclear because of the passage of time. To help clarify this situation, we developed a 435-year fire history for the former conifer forests of northern Pennsylvania. Ninety-three cross sections of fire-scarred red pines (Pinus resinosa Aiton) collected from three sites were analyzed to determine common fire regime characteristics. Prior to European settlement, fires occurred every 35–50 years and were often large dormant-season burns that sometimes initiated red pine regeneration. American Indians probably ignited these fires. Fire occurrence had a weak association with multiyear droughts. After European settlement started around 1800, fires occurred every 5–7 years due to widespread logging. Fire size and seasonality expanded to include small growing-season fires. The weak drought–fire association ceased. In the early 1900s, logging ended and wildfire control began. Since then, fires have been nearly absent from the sites despite several multiyear droughts in the 20th century. The human influences of cultural burning, logging, and fire exclusion are more important than the influence of drought to the fire regimes of northern Pennsylvania.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".