Bibliographic record
Abstract
Glaciers and ice caps in Arctic Canada are continuing to lose mass at a rate that has been increasing since 1987, reflecting a trend towards warmer summer air temperatures and longer melt seasons. Ice shelf breakup is another consequence of this trend. Shrinkage of mountain glaciers and ice caps is one of the major causes of global sea level change (Meier et al., 2007). The area of mountain glaciers and ice caps in the Arctic is over 400,000 km2 – nearly half the global total – and these glaciers were responsible for 50-60 % of the sea level rise attributed to wastage of glaciers and ice caps between 1961 and 2004 (Kaser et al., 2006). The health of glaciers is measured using their annual mass balance – the difference between the amount of mass added to them each year by snowfall, and the amount removed by surface melting and meltwater runoff, and by calving of icebergs. Much of the high Arctic is very dry, with little inter-annual variability in annual snowfall. In these regions, year-to-year variability in mass balance arises mainly from changes in summer temperatures and surface melt rates. In more maritime regions like Alaska, Iceland, and Svalbard, snowfall is higher and more variable, so mass balance variability reflects both summer and winter conditions. In most regions, flow
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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".