Global Climate Change: Assessing the Importance of the Roles of Ice Cover and Glacial Changes
Bibliographic record
Abstract
Issues of water security are rapidly becoming more widely recognized as impacted. Increased levels of carbon dioxide are clearly evident and long-term temperature increases are clearly evident. These indicators are being used to compile evidence that sea level rise in the future will be between 0.3 and 1.0 m by 2100 and, combined with more severe storms along coastlines, will translate into increasing challenges for coastal cities. The enormous glaciers in Greenland and Antarctica will continue to contribute to sea level rise but fortunately, at modest levels, for thousands of years. On the other hand, land-based glaciers will continue to become depleted and the ramifications to agricultural practices are expected to be profound, with situations of significant percentages of the world’s land-based glaciers being lost by 2100. Further, the disappearance rate of the Arctic Ocean ice cover is already profoundly evident, with losses of ice cover of about 13.1 percent per decade now occurring. Rates of warming in the Arctic are increasing at two to three times the global annual average and warrant further forecasting of the implications. With the reduced ice cover, the water in the Arctic Ocean is now absorbing the energy from the sun, not reflecting the sun’s energy, thereby accelerating further ice cover melting. The result is that the jet stream is weakening and evidence is mounting that there will be increased excursions of the polar vortex causing very cold weather extremes in northern hemisphere areas.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".