Landsystem analysis of a tropical moraine‐dammed supraglacial lake, Llaca Lake, Cordillera Blanca, Perú
Bibliographic record
Abstract
Tropical glaciers of the Cordillera Blanca, Perú are rapidly thinning and retreating as a result of climate warming. The retreat of these glaciers along narrow linear bedrock valleys has increased the number and size of moraine‐dammed glacial lakes formed in the valleys. This study aims to identify the geomorphological and sedimentological characteristics of an enlarging moraine‐dammed supraglacial lake (Llaca Lake) in the Cordillera Blanca. Field‐based sedimentological observations and geomorphological mapping were combined with remotely sensed data and a photogrammetric model derived from aerial surveys by an uncrewed aerial vehicle to identify landform‐sediment assemblages. The geomorphological and sedimentological characteristics of Llaca Lake are synthesized into three landsystem zones: Zone 1: distal portions of Llaca Lake and the latero‐frontal moraine; Zone 2: the central zone of ice‐cored hummocks; and Zone 3: the active glacier margin. These zones are differentiated based on the spatial distribution of landforms, sediments, and active geomorphological processes. This is the first study to describe the landform‐sediment assemblages in a tropical moraine‐dammed supraglacial lake system and provides a framework for further landsystem element analysis of these growing supraglacial lakes in rapidly deglaciating high‐altitude environments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".