A ∼5000 year multiproxy record of summer climate in NE Greenland
Bibliographic record
Abstract
The High Arctic plays a vital role in Earth's climate system, and its ecosystems are highly sensitive to global climate change. High Arctic lakes are valuable sentinels of climate change, as their sediments integrate long-term natural climatic fluctuations and anthropogenic influences. Here, we present a high-resolution ∼5000 year-reconstruction of NE Greenland climate variability from Aucella Lake (74°N, 20°E) based on physical, chemical, and biological properties of lake sediments. We use CT-scans, hyperspectral imaging, organic matter, XRD, and diatom analyses to show that changing air temperatures were controlled by a mix of regional climatic changes and local landscape feedbacks. The latest Mid-Holocene (∼5.0-3.8 cal. ka BP) was characterized by relatively warmer conditions, while the onset of the Late-Holocene was marked by abrupt temperature decreases that coincided with the beginning of glacial advances elsewhere (∼3.8-3.4 cal. ka BP). From ∼3.4-2.4 cal. ka BP, the sedimentary record indicated progressive warming, with temperature peaking during the Medieval Climate Anomaly, although temperature rises were punctuated by abrupt, short-lived cold periods. From ∼1.1-0.05 cal. ka BP, the influence of landscape factors over the system diminished. Sedimentary indicators suggested a transition towards a colder, more humid climate, coinciding with the beginning of the Little Ice Age, that was characterized by a marked decrease in air temperature that reached minimum values at the end of this period. The last 50 years at Aucella Lake were marked by abrupt temperature rises, consistent with recently observed anthropogenic global warming. Our results illustrate the importance of high-resolution multiproxy studies for accurately characterizing lake linkages to their environment and climate.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".