Winter severity index using widely available weather information
Bibliographic record
Abstract
Context Changes in global climate and evidence of species’ responses to these changes have increased interest in relationships between climate variables and species demography and distributions. Although an important tool for many ecological questions, large-scale climate indices fail to provide the spatial resolution necessary to investigate drivers of change across small spatial scales. Climate variables that describe yearly climate variation at large spatial extents and small spatial grain are needed. Aim Here we develop a model for snow depth using snow water equivalent (SWE) data, which are readily available in a number of formats, to be included in a more general climate index. We use an existing winter severity index (WSI) for white-tailed deer to test the performance of the model. Methods We obtained data for 13 weather stations from north-western Canada, reporting both SWE and snow depth. We accumulated a snowpack from daily SWE of snowfall and then tested two methods for converting the SWE of the snowpack into the snow portion of the WSI. We then generalised the model for application to the northwest forest climate region. Key results Coefficients of determination (R2) relating the actual and predicted snow depth portion of the WSI ranged from 0.41 to 0.78, with only three stations being below 0.50. Coefficients of determination (R2) relating the actual and predicted WSI for the northwest climate region ranged from 0.58 to 0.88. Conclusions The SWE model predicts the snow portion of the WSI well for most stations and, when incorporated into the full WSI, provides a good measure of relative winter severity across space and time for most stations. Implications The method developed here could be applied elsewhere, where snow depth is an important factor in species ecology. The benefit of this approach is a comparatively simple method that maximises the use of widely available SWE data in place of snow-depth data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 0.001 |
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".