Explaining mass balance and retreat dichotomies at Taku and Lemon Creek Glaciers, Alaska
Bibliographic record
Abstract
Abstract We reanalyzed mass balance records at Taku and Lemon Creek Glaciers to better understand the relative roles of hypsometry, local climate and dynamics as mass balance drivers. Over the 1946–2018 period, the cumulative mass balances diverged. Tidewater Taku Glacier advanced and gained mass at an average rate of +0.25 ± 0.28 m w.e. a–1, contrasting with retreat and mass loss of −0.60 ± 0.15 m w.e. a−1 at land-terminating Lemon Creek Glacier. The uniform influence of regional climate is demonstrated by strong correlations among annual mass balance and climate data. Regional warming trends forced similar statistically significant decreases in surface mass balance after 1989: −0.83 m w.e. a–1 at Taku Glacier and −0.81 m w.e. a–1 at Lemon Creek Glacier. Divergence in cumulative mass balance arises from differences in glacier hypsometry and local climate. Since 2013 negative mass balance and glacier-wide thinning prevailed at Taku Glacier. These changes initiated terminus retreat, which could increase dramatically if calving begins. The future mass balance trajectory of Taku Glacier hinges on dynamics, likely ending the historic dichotomy between these glaciers.
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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 | 0.001 |
| 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 | 0.001 |
| 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".