Understanding glacier processes to decode the drivers of glacier change
Bibliographic record
Abstract
Detection, attribution and projection of glacier and ice-sheet change characterize much of our community’s work, motivated in part by the associated impacts ranging from local hazards to regional water supply to global sea-level rise. Toward improved attribution of glacier change on local to regional scales, I profile work aimed at discerning the internal versus external drivers of glacier behaviour through process-oriented studies. Using examples from northern Canada, combining observational and numerical approaches improves our understanding of fundamental processes that define the boundary conditions at the ice interface with bedrock, water and atmosphere. These studies have allowed us to revisit questions related to glacier surging, hydrology and ice-dammed lakes and the co-evolution of glacier geometry and thermal structure, with occasionally surprising and counter-intuitive results.While the internal dynamics of glacier systems have the potential to confound the climate signal on societally relevant timescales, the direct effects of climate via surface mass balance remain as important as ever. Improved observational platforms, advances in modelling and the growing abundance and availability of remotely sensed data have amplified our capacity to study these systems, and more generously than ever reveal information archived by glacier processes. Using these tools, we are now beginning to disentangle the contributions of the geologic substrate, environmental setting, internal ice dynamics and climate forcing to observed glacier change in globally significant ice-rich parts of the world.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".