The Holocene thermal maximum and late-Holocene cooling in the tundra of NE European Russia
Bibliographic record
Abstract
Abstract To investigate the Holocene climate and treeline dynamics in the European Russian Arctic, we analysed sediment pollen, conifer stomata, and plant macrofossils from Lake Kharinei, a tundra lake near the treeline in the Pechora area. We present quantitative summer temperature reconstructions from Lake Kharinei and Lake Tumbulovaty, a previously studied lake in the same region, using a pollen–climate transfer function based on a new calibration set from northern European Russia. Our records suggest that the early-Holocene summer temperatures from 11,500 cal yr BP onwards were already slightly higher than at present, followed by a stable Holocene Thermal Maximum (HTM) at 8000–3500 cal yr BP when summer temperatures in the tundra were ca. 3°C above present-day values. A Picea forest surrounded Lake Kharinei during the HTM, reaching 150 km north of the present taiga limit. The HTM ended with a temperature drop at 3500–2500 cal yr BP associated with permafrost initiation in the region. Mixed spruce forest began to disappear around Lake Kharinei at ca. 3500 cal yr BP, with the last tree macrofossils recorded at ca. 2500 cal yr BP, suggesting that the present wide tundra zone in the Pechora region formed during the last ca. 3500 yr.
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 | 0.001 | 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".