Ice cliff contribution to the tongue-wide ablation of Changri Nup Glacier, Nepal, central Himalaya
Bibliographic record
Abstract
Abstract. Ice cliff backwasting on debris-covered glaciers is recognized as an important mass-loss process that is potentially responsible for the “debris-cover anomaly”, i.e. the fact that debris-covered and debris-free glacier tongues appear to have similar thinning rates in the Himalaya. In this study, we quantify the total contribution of ice cliff backwasting to the net ablation of the tongue of Changri Nup Glacier, Nepal, between 2015 and 2017. Detailed backwasting and surface thinning rates were obtained from terrestrial photogrammetry collected in November 2015 and 2016, unmanned air vehicle (UAV) surveys conducted in November 2015, 2016 and 2017, and Pléiades tri-stereo imagery obtained in November 2015, 2016 and 2017. UAV- and Pléiades-derived ice cliff volume loss estimates were 3 % and 7 % less than the value calculated from the reference terrestrial photogrammetry. Ice cliffs cover between 7 % and 8 % of the total map view area of the Changri Nup tongue. Yet from November 2015 to November 2016 (November 2016 to November 2017), ice cliffs contributed to 23±5 % (24±5 %) of the total ablation observed on the tongue. Ice cliffs therefore have a net ablation rate 3.1±0.6 (3.0±0.6) times higher than the average glacier tongue surface. However, on Changri Nup Glacier, ice cliffs still cannot compensate for the reduction in ablation due to debris-cover. In addition to cliff enhancement, a combination of reduced ablation and lower emergence velocities could be responsible for the debris-cover anomaly on debris-covered tongues.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".