Sensitivity of alpine glacial change detection and mass balance to sampling and datum inconsistencies
Bibliographic record
Abstract
Abstract. Glacial mass balance estimated through the geodetic method requires glacial surface coordinate observations from historical and contemporary sources. Contemporary observations and historical topographic maps are typically referenced to separate horizontal and vertical datums and observed with different sampling intervals. This research demonstrates the sensitivity of glacial change detection to the datum considerations and sampling schemes through case studies of Andrei, Bridge and Peyto glaciers in Western Canada. To simulate the procedure of observing the glacial surfaces, profile lines were sampled from Digital Elevation Model (DEMs) on contour intervals for historical data and horizontal intervals for contemporary data. Profile lines from the following scenarios were compared: (1) different horizontal and vertical sampling schemes; (2) the horizontal datum was correctly reconciled but the vertical datum was not; (3) the vertical datum was correctly reconciled but the horizontal datum was not; (4) both the horizontal and vertical datums were correctly reconciled; and (5) both the horizontal and vertical datums were incorrectly reconciled. Vertical errors of up to 6.9 m, 6.0 m and 5.0 m were observed due to sampling effects and vertical errors of 22.2 m, 9.9 m and 55.0 m were observed due to datum inconsistencies on Bridge, Andrei and Peyto glacier respectively. Horizontal datum inconsistencies manifested as erratic levels of growth or downwasting along the glacial surface profile and vertical datum errors manifested as a consistent vertical offset. Datum inconsistencies were identified to contribute errors of up to 257.2 × 106 m3 (or 87%) and 54.6 × 106 m3 (or 580%) of estimated volume change below and above the equilibrium line respectively on Peyto Glacier. The results of this study provide an estimate of typical errors due to sampling constraints or datum inconsistencies as well as guidance for identifying where these error sources have contaminated mass balance results.
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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 | 0.013 | 0.076 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".