Instream flow determination using a multiple input fuzzy‐based rule system: a case study
Bibliographic record
Abstract
Abstract The attempts made to manage water to meet human requirements should also consider the needs of freshwater species and ecosystems. There are many tools available to assess instream flow needs, one of which is the use of habitat preference models. In this study, a fuzzy approach was used for modelling habitat preferences for two life stages of Atlantic salmon (Salmo salar). Experienced fish biologists and technicians contributed to the development of fuzzy sets and fuzzy preference rules for spawning and parr habitat. Fuzzy sets were defined for water depth, velocity and substrate composition. Fuzzy preference rules for the two life stages were then defined as sets of IF–THEN rules relating the physical attributes to habitat suitability. The fuzzy suitability indices are then used to obtain weighted usable area (WUA) at different discharges and to estimate the ecologic flow required to preserve habitat. Different methods are applied to combine the membership function and rules defined by the experts. A sensitivity analysis of rules of the combined system indicated that a limited number of rules are determinant and results are highly dependent on the consequences of these rules. A modification in the consequence of these rules can significantly alter WUA estimations. It is therefore recommended to combine the knowledge of many experts in the elicitation process and to quantify the uncertainty associated with the combination of expert knowledge. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".