Drainage and agriculture impacts on fire frequency in a southern Illinois forested bottomland
Bibliographic record
Abstract
Postsettlement (1909–2003) fire history of a forested bottomland in the Mississippi Embayment of southern Illinois, USA, was determined using fire-scar analysis. The study area is a forested bottomland hardwood site, with remnant pockets of the dominant presettlement bald cypress – tupelo (Taxodium–Nyssa) vegetation. Ditch drainage was installed in 1919, with agricultural clearing and abandonment varying throughout the early and mid-twentieth century. Commercial agricultural activities ceased after the site became part of a conservation area ca. 1950. The hydrology of the site was further modified in 1957 when it was inundated for waterfowl management. Both drainage and land clearing for agriculture were associated with increased fire frequency. Although drainage was a necessary precursor to agriculture across much of this landscape, land improvement played the stronger role in determining fire frequency. The mean fire interval for the study period (1895–1965) was 1.73 years, with a minimum of 1 year and a maximum of 15 years. This frequency contrasts with the complete fire exclusion that has prevailed in the area since 1965. These results have important implications for the maintenance and restoration of forested wetland ecosystems where the present fire regime differs dramatically from that under which the now-dominant forest vegetation developed.
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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 | 0.000 |
| 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 | 0.000 |
| 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".