Investigating correlations between snowmelt and forest fires in a high latitude snowmelt dominated drainage basin
Bibliographic record
Abstract
Abstract High latitude drainage basins are experiencing increases in temperature higher than the global average, with snowmelt dominated basins most sensitive to effects in winter because of the snowpack's integration of these changes over the season. This may influence the timing of snowmelt onset, the melt‐refreeze period and snowpack accumulation resulting in changes in spring runoff, associated flooding and drought conditions later in the year, possibly enhancing forest fire potential. Large burned areas cleared of vegetation change discharge dynamics and may affect snowmelt characteristics and discharge in subsequent seasons. Correlations are tested by comparing forest fire occurrence with spring melt onset, the end of the melt‐refreeze period (after which snow rapidly depletes) and early snowmelt events. Snow characteristics are derived from brightness temperature ( T b ) data from the Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) for 2003–2010. Dates of melt onset, end of melt‐refreeze and early melt events are defined with T b and diurnal amplitude variation thresholds. Areas and intensities of forest fires are from the Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly data (MOD14), and all data are mapped to an Equal‐Area Scalable Earth Grid to assess spatial correlations. Earlier melt onset and end of melt‐refreeze are found in years and areas of high forest fire occurrence by comparing high (2004–2005) and low (2006–2007) fire years in the Porcupine sub‐basin of the Yukon River in northeastern Alaska and the Yukon Territory. The burned areas also correlate with relatively later melt onset and later end of melt‐refreeze in subsequent low fire years. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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".