Cataloguing of rock glaciers in dissimilar regions of the Mackenzie Mountains
Bibliographic record
Abstract
Rock glaciers have been the subject of extensive research in recent years, due to their potential to serve as indicators of past and present climate conditions and their potential impacts on water resources. Compilation and analysis of collected data on the location, size, and characteristics of rock glaciers within the Mackenzie Mountains was used to build a rock glacier catalogue that will serve as a valuable resource for future research and monitoring efforts. The research also aims to map the spatial distribution of rock glaciers using optical imagery and to develop a semi-automated detection model using Generalized Additive Models (GAMs) in R. The model will incorporate attribute data, such as solar radiation, aspect, topographic position index, slope, elevation, and lithology as controls for rock glacier development. Topographic data was collected in multiple regions of the Mackenzie Mountains and extracted using a 30m digital elevation model (DEM). The results of this study have the potential to improve our understanding of rock glacier distribution and dynamics in the Mackenzie Mountains and could also be applied to similar mountainous regions. This is the WDCAG Conference 2023 Award Winner for Best Masters Poster.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".