Two Late Quaternary Pollen Records from South-Central Alaska
Bibliographic record
Abstract
Pollen records from Wonder and Ten Mile lakes, located at aititudinal treeline to the north and south of the Alaska Range respectively, document the vegetation history of a portion of the southern Alaskan boreal forest. The new pollen diagrams indicate a Betula shrub tundra, preceded at Wonder Lake by a sparse herb tundra, which characterized these two areas during latest Wisconsinan times. Populus was in the vicinity of Ten Mile Lake ca. 10,000 BP, but was apparently absent from Wonder Lake. Picea glauca grew at or near Ten Mile Lake by 9100 BP, with P. mariana becoming important ca. 7000 BP. The first forests at Wonder Lake were also dominated by P. glauca and followed by increased numbers of P. mariana. The timing of forest establishment at Wonder Lake is uncertain due to problematic radiocarbon dates. Alnus appears to be common in both regions by ca. 7000 BP. These records suggest that paleo-vegetational reconstructions are more difficult for the southern than northern boreal forests in Alaska because of greater topographic diversity, difficulties with over-representation of some pollen taxa, and problems with radiocarbon dating. Despite these concerns, available data from south-central Alaska suggest that southern and northern forests differ in their vegetational histories. Such differences, when related to temperature fluctuations that have been postulated for the Holocene, imply that the Alaskan boreal forest may not respond uniformly to future global warming.
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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 | 0.001 | 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".