Measuring daily surface elevation and velocity variations across a polythermal arctic glacier using ground-based photogrammetry
Bibliographic record
Abstract
Abstract Twin time-lapse cameras were set up to provide a convergent view of ten targets located on the surface of an Arctic glacier, near its terminus. Using photogrammetric analysis, daily target elevations were calculated over three winters and two ablation seasons. Results show that the glacier surface level dropped by approximately 2.9 and 2.5 m respectively over the 2009 and 2010 ablation seasons. GPS measurements suggest the associated average vertical errors were 0.105 m and 0.04 m respectively. Photogrammetric measurements indicated that thinning from melting exceeded surface gains due to ice inflow by a factor of ~ 5: 1 during 2009 and 2010. Horizontal flow rates of 6–12 mm d–1 were measured photogrammetrically during autumn 2008 and compared to interpolated winter rates established from GPS measurements, with differences ranging between 7.4% and 17.2%. The availability of a continuous series of data for the duration of the study allowed the start and end dates of each ablation season to be identified, making it possible to determine the length of each balance year. The results show the utility of time-series photogrammetry for observing surface elevation changes and dynamic processes in remote glacial environments.
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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 | 0.000 | 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".