Bibliographic record
Abstract
ABSTRACT Philosophically, the natural flow regime concept is tremendously appealing; however, its application can be challenging for many biologists without the expertise or resources to handle such approaches on their own. This is particularly true on hydropeaking rivers, where incorporating natural flow is sometimes challenging. Additional challenges include our limited understanding of how individual flow components relate to geomorphic and ecological processes. Supplementary to environmental flow approaches is understanding that many hydropeaking rivers are ecologically two different rivers in one: the low flow and high peaking flow. Taxa that require a narrow range of water velocities or cannot withstand rapid changes in discharge would likely be eliminated or competitively disadvantaged under such harsh environmental conditions. As the low and peak flows diverge, the two rivers become increasingly different ecologically, and there will likely be fewer taxa that can withstand such abiotic variability. Deviations from a natural flow regime may result in new constraints on certain fishes and invertebrates, but this does not necessarily mean a loss of productive fish habitat. Viewing hydropeaking rivers as two rivers in one and the risks associated with high to low flow ratios may serve as a more practical and useful perspective towards maintaining altered yet productive rivers while representing a step towards improving the management river ecosystems. © Her Majesty the Queen in Right of Canada 2013.
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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 | 0.001 | 0.004 |
| 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 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".