SELECTING HYDROLOGIC INDICES FOR THE PRAIRIE PROVINCES
Bibliographic record
Abstract
ABSTRACT The large and accumulating body of evidence for both the controlling effect of the flow regime on river ecology and for the dependence of river health on the natural flow regime has led to the increasing use of hydrologic indices in instream flow studies. The myriad of collinear hydrologic indices present a daunting challenge to water managers trying to select a manageable number of indices for use in a hydrology‐based environmental flow framework. In this study, a large number of hydrologic indices were calculated from gauging sites in the prairie provinces of Canada. Principal component analysis (PCA) and two rank‐based non‐parametric techniques are compared in their ability to select a small number of statistically informative indices. Despite the data being skewed and far from normal, PCA and the non‐parametric technique called BioEnv + stepwise (BEST) both led to similar interpretations and could identify a small number of indices that capture a majority of the statistical variability. BEST selected indices more evenly from among conceptual categories of flow than PCA. Copyright © 2011 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".