Instream Flow Assessment Modelling: Combining Physical and Behavioural-Based Approaches
Bibliographic record
Abstract
Most state-of-the-art applications of habitat modelling rely on three-dimensional channel topography and two-dimensional hydrodynamic models that have centered on the simple extension of one-dimensional physical habitat-based concepts (i.e., extension of the Physical Habitat Modelling System - PHABSIM). However, as demonstrated in this paper, these approaches can be extended to more realistically incorporate both the physical habitat-based metrics of streamflow and behavioural decision rules that can incorporate fish community structure and dynamics. This approach evaluates the spatial suitability of physical habitat-based on the incorporation of behavioural rule sets, such as the association with escape cover type and distance, exclusion zones associated with predators or linear dominance hierarchies, and lag-time dependence of macroinvertebrate re-colonization of habitats associated with varial zones induced by peaking/load following operations of hydropower facilities. In the paper we lay the foundation between field data collection strategies for characterization of the physical domain, and modelling the hydraulic properties of flow using two- or three-dimensional hydraulic models. We then describe the use of GIS for the integration of additional factors such as substrate, cover, distance to cover and distance to water’s edge factors. We then demonstrate how GIS, in conjunction with decision based habitat algorithms, can be used with classical-based physical habitat modelling to incorporate behavioural-based rules to evaluate escape cover, exclusion zones and lag-time dependence for macroinvertebrate under variai zone induced impacts. These examples are based on results from studies in regulated river systems in which these techniques have been shown to improve the assessment of impacts/benefits associated with altered flow regimes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".