Multi‐proxy analysis of early Holocene vegetation dynamics on the islands of proglacial Lake Ojibway (northeastern America)
Bibliographic record
Abstract
ABSTRACT Following deglaciation, lowland sites in eastern Canada that were covered by proglacial Lake Ojibway recorded direct afforestation with boreal mixedwood taxa, without an initial tundra phase, contrary to sites that were never covered by this lake. Because former islands of proglacial Lake Ojibway were colonized by vegetation before lake drainage around 8200 cal a bp, we hypothesized that these paleo‐islands went through a non‐arboreal phase which eventually transitioned to boreal mixedwoods, hence providing a source of propagules for rapid and later colonization of the lowlands. We carried out a multi‐proxy analysis combining pollen, macro‐remains, sedimentary ancient DNA and charcoal to document vegetation composition and dynamics on two paleo‐islands. Both study sites recorded progressive vegetation establishment starting with a tundra‐like phase with only herbs and shrubs, followed by open forests dominated by either larch or pine, culminating with the establishment of the boreal mixedwoods about 300 years before the final drainage of proglacial Lake Ojibway. Fire regimes seem to have partially driven vegetation dynamics and diversity on the paleo‐islands. Paleo‐islands were sources of propagules, which helps explain how the former Lake Ojibway lowlands were directly colonized by boreal mixedwoods, without an initial tundra phase.
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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".