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 km 2 above 3400 m), Colorado, where we obtained a total rock glacier surface area of 70 km 2 corresponding to a water equivalence in the order of 0.50–0.76 km 3 . Estimated rock glacier debris volumes imply postglacial denudation rates on the order of 0.5–1.1 mm yr −1 within 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".