Presence of lakes and wetlands decreases resilience of jack pine ecosystems to late-Holocene climatic changes
Bibliographic record
Abstract
We reconstructed vegetation and fire histories from four sites located on a sandy outwash plain in northwestern Wisconsin (USA) to test whether lakes and wetlands have influenced how vegetation and fire regimes in pine–oak forests responded to late-Holocene climatic changes. Because of positive feedbacks between jack pine (Pinus banksiana Lamb.) and fire, communities with few fire breaks should be more resilient to changing climatic conditions. Pollen and charcoal from lake-sediment cores were used to reconstruct vegetation changes at 50- to 100-year intervals and forest fire history at decadal time scales for the past 2500 years. The presence of fire breaks affected both fire regimes and the response of vegetation to climatic changes. Areas with more fire breaks had smaller charcoal peaks and the vegetation was more responsive to climate changes. The vegetation in areas with few fire breaks was more resilient, maintaining higher amounts of jack pine and (or) red pine than the more protected sites. We interpret these findings as evidence that positive feedbacks between fire and jack pine forests stabilized vegetation at sites where fire breaks were absent, and that such sites may be relatively resilient to future climate changes, until jack pine is no longer able to regenerate under the regional climatic conditions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".