Interpretation of the glacial lake outburst floods database in relation to climatic conditions in different world regions
Bibliographic record
Abstract
This article investigates the response of glacial lake outburst floods (GLOFs) to climatic conditions since the beginning of the 20th century and during individual seasons based on the data from the publicly available online database recording past GLOFs worldwide. All recorded GLOFs were classified into the regions of Alaska, Western Canada and USA, Central Andes, Southern Andes, Iceland, Scandinavia, Alps, Caucasus, Tian Shan, Central Asia I (west), Central Asia II (east), and New Zealand. In each of these regions, the influence of temperature and precipitation on the frequency of glacial flood occurrences was investigated. It was established that GLOFs occur mainly during the summer months and air temperature is their main triggering factor. Since the frequency of GLOFs is influenced by both temperature and precipitation, a gradual increase in the frequency of GLOFs is expected because of global warming, although the relative importance of each factor will vary across regions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".