Pollen evidence for major shifts in forest composition since the mid‐nineteenth‐century in the vicinity of Sudbury, Ontario
Bibliographic record
Abstract
Numerous anthropogenic stressors have impacted the region surrounding Sudbury, Ontario, leading to pronounced vegetation and landscape change. Few long‐term records exist to understand the nature or timing of this change. We use pollen analysis from radiometrically dated sediments of Clearwater Lake to compare pre‐ and post‐settlement vegetation. Beginning ∼1850 CE, the record shows major shifts in forest composition, coincident with settlement and the beginnings of lumbering. These changes are unprecedented for the past ∼5000 years, and consist of increases in diversity and abundance of deciduous tree taxa and herbaceous disturbance indicators. While evidence of mining appears as early as 1900 CE, little effect is seen in the pollen record until ∼1930 CE, when sedimentation rates increased and acidification of the lake also began. At this time, further increases in palynological disturbance indicators and minimum sediment organic matter levels indicate the period of maximum vegetation loss. As a result of reduced emissions since the 1970s, water quality began to improve in Clearwater Lake and there are some decreases in the abundances of shade‐intolerant disturbance indicators in the pollen record. However, the fact that the pollen assemblages do not resemble those prior to 1850 suggests lasting vegetation changes.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".