A comparison of methods for evaluating instream flow needs for recreation along rivers in southern Alberta, Canada
Bibliographic record
Abstract
Abstract Four methods were compared for determining recreational instream flow needs (R‐IFN) for paddling canoes, kayaks and rafts on ten river reaches in the Oldman River Basin of southern Alberta. Two flow criteria were evaluated: ‘minimal flow’—the low flow that still provides a reasonable quality river trip; and ‘sufficient flow’—the lower end of the favoured flow range. A voluntary, mail‐in user survey from 1983 to 1997 produced 394 responses (4251 paddler days) relative to flow suitability. An expert judgment approach considered flow recommendations from three regional paddling guides that were considered comprehensive and credible. A flow comparison involved about 20 paddle trips per reach by the authors with differing groups, boats and flows. These subjective approaches produced quite consistent results ( r 2 = 0.63) and these were compared to results from an objective, hydraulic modelling method, the ‘depth, discharge method’ (DDM), that applied stage–discharge functions to determine flows that would satisfy depth criteria of 60 and 75 cm. The DDM minimal flows were closely correlated with the means of the subjective methods ( r 2 = 0.73). Thus, all four approaches produced generally consistent results, indicating that all methods were valid. Typical minimal and sufficient flows were about 15 and 30 m 3 s −1 , respectively, for the medium‐sized river reaches that had average annual discharges (mean Q ) of about 20 m 3 s −1 . A close correlation ( r 2 = 0.90) between the minimal flow and mean Q suggests that mean Q can provide an initial estimate for R‐IFN for rivers of this type and size. We recommend that R‐IFN studies commence with the DDM since it is quick, inexpensive and objectively defensible. This would provide guidelines for subsequent subjective assessments that should involve more than one approach to increase the breadth of subjective consideration. Copyright © 2003 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 | 0.000 | 0.000 |
| 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".