High-Resolution Pollen and Charcoal Records from Fish Lake, New Brunswick, Canada
Bibliographic record
Abstract
The Acadian forest is a mixed-wood forest covering the Canadian provinces of New Brunswick and Nova Scotia. Picea rubens (red spruce) is its signature species which has been prominent for circa 2,000 years. To our knowledge, no high-resolution pollen analysis has been done in New Brunswick, and no lacustrine charcoal analysis. We extracted a 124 cm surface core from Fish Lake, (46° 8’ 38.32’’N, 66° 53’ 12.64’’ W), near Fredericton, New Brunswick, Canada. A BACON age-depth model, based upon 4 14C and 15 210Pb dates, showed that the bottom of the core dated to AD 890. We performed a high-resolution pollen analysis on this core at a ~10 year resolution, with 125 samples in all. Over the last millennium, there were declines in Betula (birch), Tsuga canadensis (eastern hemlock) and Fagus grandifolia (American beech), together with increases in Pinus sp. (pine), Abies balsamea (fir) and Picea sp. (spruce), including P. rubens. CONISS showed that the Medieval Climate Anomaly (MCA – AD 900-1400), Little Ice Age (LIA – AD 1400 –1850), and the European settlement period were clearly demarcated in the pollen record. A rise in Ambrosia (ragweed) marked early Acadian French agriculture at ~AD 1680. The last 300 years of the European period showed increases in Poaceae (grasses), Ambrosia, and other herbs, and declines in Pinus sec. haploxylon (eastern white pine). Charcoal analysis showed that natural forest fires had a continuous presence over the past millennium. The paleo-fire record showed higher fire frequency during the MCA than in the LIA. WA-PLS was used to reconstruct spring temperatures. The reconstruction showed that the MWP had an average spring temperature of 3.2 °C, and the LIA had an average spring temperature of 2.2 °C.
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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 | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".