Attribution of snowmelt onset in Northern Canada
Bibliographic record
Abstract
Abstract In the region of Earth most sensitive to climate change, spring snowmelt serves as a measurable indicator of climate change and plays a strong role in the feedbacks that amplify Arctic warming. We characterize the melt season and attribute melt onset in a region of northern Canada during the spring snowmelt season from 2003 to 2011. Melt onset dates are obtained from Advanced Microwave Scanning Radiometer for the Earth Observing System retrievals. Energy balance and meteorological fields are obtained from NASA's Modern Era Retrospective Analysis for Research and Applications product. Analysis of three distinct subregions demonstrates that typical values of energy balance terms vary across the region and have different roles in melt attribution. Melt is controlled more by advective energy farther southwest where melt onset begins sooner, compared to higher levels of radiative energy over the tundra. This study demonstrates that a relatively small region can exhibit large differences in controls on spring snowmelt both within the region and interannually, and these differences can be understood in the context of factors ranging from the large‐scale synoptic pattern to land cover and the local energy balance. Being able to attribute melt onset to those drivers that are changing as the high latitudes warm as opposed to those that do not (i.e., insolation) allows better long‐term prediction of melt season dynamics and the climatological processes influenced by snow cover and its feedbacks.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".