Dendrogeomorphic reconstruction of snow avalanche regime and triggering weather conditions
Bibliographic record
Abstract
While dendrogeomorphology has been recognized as a useful tool to identify past avalanche activity, there is only a handful of papers that focus on the assessment of weather or climatic triggers of tree-ring reconstructed avalanche events. This paper compares the potential of logistic regression and classification tree algorithms to highlight weather scenarios responsible for the occurrence of high-magnitude avalanche activity in the Presidential Range of the White Mountains, New Hampshire (USA). Our tree-ring procedure improves the modern GD- I t threshold with the implementation of a second criteria based on the Moran index. 450 trees sampled in seven different avalanche paths allowed us to reconstruct 45 avalanches that occurred during 19 different years for the period 1936–2012. The results show that while statistically significant, the logistic regression models are less accurate than classification trees to assess avalanche activity based on annual and monthly weather variables. Moreover, even if snow related covariates are located at the root node of every classification tree model, the addition of temperature and wind predictors increases their robustness. This suggests that high-magnitude avalanches in the Presidential Range not only respond to snow, but also to atmospheric conditions responsible for the creation of weak layers within the snowpack.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".