Statistical analysis of topographic and climatic controls and multispectral signatures of rock glaciers in the dry Andes, Chile (27°–33°S)
Bibliographic record
Abstract
Abstract The dual nature of rock glaciers as ice‐rich mountain permafrost and sediment storage systems results in a combination of geomorphic processes and energy balance components controlling their distribution. We use the generalised additive model (GAM), a semi‐parametric nonlinear method, to empirically analyse environmental controls and spectral characteristics of rock glaciers in the dry Andes of Chile based on presence/absence data at random point locations and predictor variables derived from digital elevation models and Landsat data. A combination of nonlinearly transformed local and catchment‐related terrain attributes (especially local and catchment slope and potential incoming solar radiation, PISR) characterises the geomorphic and climatic niche of rock glaciers. The influence of (latitude adjusted) relative PISR varies with mean annual air temperature (MAAT): high‐PISR sites are favourable for rock glacier development at lower MAATs and low‐PISR sites at higher MAATs. TM/ETM+ band 6 (thermal infrared) is an additional nonlinear predictor. The combination of topographic, climatic and multispectral data in a GAM achieves an excellent general discrimination (area under the ROC curve 0.87 on the model domain and 0.94 overall). In automatic rock glacier detection at a sensitivity of 70 per cent, this model achieves a false‐positive rate (FPR) of 6.0 per cent overall and 12.8 per cent on the model domain (bootstrap estimates: 7.9% and 16.8%). Dropping the multispectral data significantly increases the bootstrapped FPR by 36 per cent. Thus, the fusion of multisource data using modern nonlinear classification techniques is a promising step towards automatic rock glacier detection. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".