A 1600-year diatom record of hydroclimate variability from Wolf Lake, New York
Bibliographic record
Abstract
A high-resolution diatom record from Wolf Lake, a minimally disturbed ‘heritage’ lake, provides insights into the hydroclimatic history of the Adirondack Mountains of northern New York during the last c. 1600 years. Three pronounced dry periods occurred during c. AD 490–610, 780–870, and 1010–1080, and low precipitation generally prevailed during the warm Medieval Climate Anomaly ( c. AD 950–1350), a finding that fills an important gap in knowledge of the spatial extent of droughts across North America during that period. During the cooler ‘Little Ice Age’ interval ( c. AD 1350–1800), inferred water balance was generally more positive. Seven peaks in charcoal abundance represent fire events during both wet and dry periods. Unusually high charcoal and inorganic sediment deposition c. AD 1700 could reflect human activity in the watershed, as might an abrupt rise in the relative abundances of planktonic and tychoplanktonic diatoms in Wolf Lake during the AD 1860s. The diatom record displays periodicities of c. 256 and 512 years in addition to high-frequency fluctuations, suggesting that significant precipitation variability is likely to continue to disrupt climatic trends in this region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".