A 750-yr record of autumn snowfall and temperature variability and winter storminess recorded in the varved sediments of Bear Lake, Devon Island, Arctic Canada
Bibliographic record
Abstract
The varve record from High Arctic, proglacial Bear Lake reveals a regionally coherent hydroclimatic signal as well as complexities due to changing hydroclimatic and limnologic conditions. Varve formation is strongly dependent on underflows that exhibit variability in strength during the past 750 yr. Periods with reduced underflow sedimentation and accumulation rates fail to produce varves in the distal part of the lake. Isolated coarse silt and sand grains occur in 80% of the varves and are interpreted to be niveo-aeolian in origin. Coarse (>500 μm) sand grains deposited on the lake ice by strong winter winds are notably less common since A.D. 1850, likely due to reduced storminess. Regression of the varve thickness record with meteorological records indicates high correlations with autumn (September and October) temperatures and total monthly snowfall. These correlations are best at times when underflow activity is sufficiently strong to produce varves throughout the lake. The close association with warmer temperatures and snow-bearing synoptic systems moving north in Baffin Bay suggests that the primary climate signal in the varves is varying autumn snow pack that controls nival discharge in the following year. The similarity between the other records of melt season temperature and sea-ice cover and the Bear Lake record suggests that summer and autumn conditions were generally similar across the Baffin Bay region through much of the last millennium.
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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 | 0.001 | 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".