Hydroclimate controls over seasonal sediment yield in two adjacent High Arctic watersheds
Bibliographic record
Abstract
Abstract Interannual variations in seasonal sediment transfer in two High Arctic non‐glacial watersheds were evaluated through three summers of field observations (2003–2005). Total seasonal discharge, controlled by initial watershed snow water equivalence (SWE) was the most important factor in total seasonal suspended sediment transfer. Secondary factors included melt energy, snow distribution and sediment supply. The largest pre‐melt SWE of the three years studied (2004) generated the largest seasonal runoff and disproportionately greater suspended sediment yield than the other years. In contrast, 2003 and 2005 had similar SWE and total runoff, but reduced runoff intensity resulted in lower suspended sediment concentrations and lower total suspended sediment yield in 2005. Lower air temperatures at the beginning of the snowmelt period in 2003 prolonged the melt period and increased meltwater storage within the snowpack. Subsequently, peak discharge and instantaneous suspended sediment concentrations were more intense than in the otherwise warmer 2005 season. The results for this study will aid in model development for sediment yield estimation from cold regions and will contribute to the interpretation of paleoenvironmental records obtained from sedimentary deposits in lakes. Copyright © 2007 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 | 0.001 | 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) | 0.000 | 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 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".