Evaluating Uncertainty in Physical Habitat Modelling in a High‐Gradient Mountain Stream
Bibliographic record
Abstract
Abstract Predictions of habitat‐based assessment methods that are used to determine instream flow requirements for aquatic biota are uncertain, but instream flow practitioners and managers often ignore those uncertainties. Two commonly recognized uncertainties arise from (i) estimating the way in which physical habitat within a river changes with discharge and (ii) the suitability of certain types of physical habitat for organisms. We explored how these sources of uncertainty affect confidence in the results of the British Columbia Instream Flow Methodology (BCIFM), which is a commonly used transect‐based habitat assessment tool for small‐scale water diversions. We calculated the chance of different magnitudes of habitat loss resulting from water diversion using a high‐gradient reach of the North Alouette River, BC, as a case study. We found that uncertainty in habitat suitability indices for juvenile rainbow trout generally dominated uncertainty in the results of the BCIFM when large (>15) numbers of transects were used. In contrast, with small numbers of transects, variation in physical habitat among sampled transects was the major source of uncertainty in the results of the BCIFM. Presentations of results of the BCIFM in terms of probabilities of different amounts of habitat loss for a given flow can help managers prescribe instream flow requirements based on their risk tolerance for fish habitat loss. Copyright © 2015 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".