Bibliographic record
Abstract
Most northern rivers experience recurrent low flow conditions in the summer (June to September), and rivers of the Mackenzie Basin are no exception. Low flow affects water supply, poses problems for river traffic, and can adversely affect aquatic ecology. Factors that affect summer low flow, which encompasses flows below specified discharge thresholds of concern, include evapotranspiration that leads to water loss from flow-contributing areas, antecedent high flow in which peak discharge is followed by gradual recession to low flow, rainfall and local glacier melt events that interrupt low discharge, replenishments of flow from upstream drainage networks, and arbitrary termination of summer low flow at the end of September. The storage mechanism of large lakes and the regulation effect of reservoirs can produce low flow regimes that differ from those exhibited by rivers without such storage functions. For most rivers, low flow events of longer duration cause larger deficits, and events with large deficits are accompanied by lower minimum discharge. The deficit-to-demand ratio measures the extent to which river flow fails to satisfy water needs. Applying this index to rivers of the Mackenzie drainage shows the hazard of streamflow drought in the basin. Low flow attributes can be summarized by their probability distributions: Gumbel distribution for minimum discharge of events and generalized exponential distribution for event duration. By fitting theoretical distributions to recorded events, one can estimate the probability of occurrence of low flow events that did not occur in the historical past.
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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) | 0.001 | 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".