Assessing spatial change of the Kaskawulsh Glacier using historical and repeat oblique photographs
Bibliographic record
Abstract
Glaciers offer scientists valuable insights into past, present, and future climates. However, these insights are currently limited by the time depth from which the record of spatial data exists. Methods to track glacial surface area changes date back to the 1950s using aerial photogrammetric surveys. The use of satellite imagery began with the launch of Landsat 1 in 1972 and has since been the dominant source of data. Canada has the world’s largest historical mountain photograph collection dating back as far as 1888 and holds the potential for nearly a century of additional data through the georectification of historical oblique images. In this study, a systematic review and synthesis of available techniques for photographic (i.e., orthographic and oblique) analysis of glacier surface area was conducted. A trial was then run using custom University of Victoria software, the Image Analysis Toolkit, to ascertain the extent to which oblique photography can be used to assess surface area change of valley glaciers. The surface area of the Kaskawulsh Glacier in view of repeat and historical photographs was quantified in 2012 and 1900, respectively. The result from the modern oblique imagery was compared to the classification of ice derived from satellite optical imagery and revealed that the geolocation of clean and debris covered ice was consistent. The historical oblique imagery showed an increase in debris cover over 112 years, which has implications for the rate of ice melt. Further research and application in glacial studies could increase the temporal coverage of available data for improved climate modelling and foster deeper understandings of the future of Canadian alpine regions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| 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".