Statistical estimation and generalized additive modeling of rock glacier distribution in the San Juan Mountains, Colorado, United States
Bibliographic record
Abstract
Our goal is to quantify rock glacier abundance and analyze the topographic controls of rock glacier distribution patterns. For this purpose we use statistical estimation techniques and generalized additive models relating rock glacier occurrences to terrain attributes. Significant applied results include the ability to determine water equivalence and denudation rates. The statistical estimation of regional rock glacier abundance based on local interpretation of air photos is an efficient alternative to costly rock glacier inventories. These presence‐absence data (N= 2933) also allow us to analyze the partly nonlinear topographic controls on rock glacier distribution with generalized additive models. We apply these techniques in the San Juan Mountains (2874 km2above 3400 m), Colorado, where we obtained a total rock glacier surface area of 70 km2corresponding to a water equivalence in the order of 0.50–0.76 km3. Estimated rock glacier debris volumes imply postglacial denudation rates on the order of 0.5–1.1 mm yr−1within the talus sheds of rock glaciers. The distribution model shows the nonlinear factors of local slope, slope of the contributing area, local curvature, and size of the contributing area controlling the probability of rock glacier occurrence. The model yields an area under the receiver‐operating characteristics curve of 0.91, which indicates an excellent fit. On the basis of the present results the integration of terrain attributes with remote‐sensing data will be the next step toward automatic mapping of rock glaciers in vast mountain areas.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".