Bibliographic record
Abstract
Ice core and mass balance studies from glaciers, ice caps and ice sheets constitute an ideal medium for monitoring and studying present and past environmental change and, as such, make a valuable contribution to the present debate over anthropogenic forcing of climate. Data derived from 32 years of measurements in the Canadian Arctic show no significant trends in glacier mass balance, ice melt, or snow accumulation, although the mass balance continues to be slightly negative. Models suggest that industrial aerosol loading of the atmosphere should add to the warming effect of greenhouse gases. However, we have found a sharp increase in the concentration of industrial pollutants in snow deposited since the early 1950's which makes the trendless nature of our various time series surprising. Spatial differences in the nature of climatic change may account for the lack of trend in the Queen Elizabeth Islands but encourages similar investigations to this study elsewhere in the circumpolar region. A global warming trend over the past 150 years has been demonstrated from instrumental data and is evident in our ice cores. However, the ice core data and glacier geometry changes in the Canadian Arctic suggest the Arctic warming is more pronounced in summer than winter. The same warming trend is not unique when viewed in the context of changes over the past 10,000 or 100,000 years. This suggests the 150-year trend is part of the natural climate variability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".