Comparison of runoff and river flow in two large northern basins
Bibliographic record
Abstract
Abstract The magnitude and timing of water delivery in two large northern basins are analysed to clarify where runoff is generated and how their rivers acquire comparable regimes (or seasonal rhythms) of flow. These two rivers, the Mackenzie in Canada and the Yenisei in Russia, traverse similar latitudes, physiographic provinces, vegetation zones and climatic regions. Within the basins, mountainous terrain and high-precipitation sections usually yield large runoff, but low runoff comes from the plains, low plateaus and areas of aridity. Winter runoff is commonly low and snowmelt is responsible for annual peak runoff in most parts of these basins, though rainfall is a prominent runoff source in southern Yenisei. Many rivers in the drainage networks display a seasonal pattern that suggests the dominance of snowmelt to produce a spring freshet followed by a general decline in summer that diminishes to winter low flows. Regulation of reservoir outflow greatly distorts the natural flow regime. Yet, along the main river downstream of the reservoirs, the influx of tributary discharge can dilute such human influence. To truly understand how water is produced and transferred in large northern rivers, the spatial and temporal complexity of flow-generation mechanisms and storage effects need to be unravelled.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".