Millennial-scale changes in local vegetation and fire regimes on Mount Constitution, Orcas Island, Washington, USA, using small hollow sediments
Bibliographic record
Abstract
We used pollen and charcoal records from small hollows plus a network of surface samples to reconstruct stand-level vegetation and fire history at three sites on the Mount Constitution plateau of Orcas Island, Washington, USA. One record (beginning ca. 7100 calibrated years BP) is from a xeric site on the northern plateau, and two (beginning 3800 and 7650 years BP, respectively) are from mesic sites on the central and south-central plateau. Before 5300 years BP, vegetation and fire regimes at both the northern and south-central sites resembled those of current Mount Constitution forests. Around 5300 years BP, Alnus increased and Pinus decreased at the mesic south-central site, suggesting a change to moister and (or) cooler conditions, but Pinus remained dominant at or near the more xeric northern site. At both sites, charcoal deposition decreased, suggesting a decrease in fire frequency and (or) severity consistent with wetter conditions. After 2000 years BP, charcoal deposition increased at all three sites, and Pinus increased in the central and south-central sites, suggesting a return to drier conditions. Thus, stands on different sites in close proximity responded individually to the same climate change, depending on local site conditions and the ecology of the dominant trees.
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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 | 0.000 | 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".