A decision support system for improving “fish friendly” flow compliance in the regulated Okanagan Lake and River System of British Columbia
Bibliographic record
Abstract
The Okanagan Valley of British Columbia (BC) has one of the lowest per-capita water supplies in Canada. Large fluctuations in water supplies routinely induce seasonal extremes of flood and drought conditions that challenge the ability of decision makers to operate complex water management infrastructure (dams, dikes, irrigation networks, flood control channels) to satisfy competing objectives to meet human-system versus natural-system needs (e.g. protect property, irrigate land, protect aquatic biota). An audit of water management performance from 1982 to 1997 indicated frequent non-compliance of water regulation decisions with “fish friendly,” lake level and river discharge ranges specified by the 1982 Canada–BC Okanagan Basin Implementation Agreement (OBIA). Development and deployment of an environmental decision support system (EDSS) to provide real-time fish and water management tools (FWMT) to decision makers offered a potential means to improve the balance of water management decisions affecting both human and natural systems. The resultant FWMT-EDSS described here includes: a coupled set of four biophysical models of critical relationships among climate, fish and water that interact with a fifth water management rules model used to predict potential consequences of decisions for fish and other water users; a network of stations providing nearly instantaneous observations of lake elevation, river-discharge, precipitation and snowpack; a Structured Query Language (SQL) server database; an internet-accessible, graphical user interface; and a set of end users representing decision makers from government agencies, industry and local communities. FWMT provides a risk assessment framework to integrate biophysical processes, deal with multiple species and geographic locations, anticipate socioeconomic outcomes of water management decisions and increase cooperation among water users to improve fish and water management. Comparisons of observations from pre-FWMT “control” versus FWMT deployment years (n = 20 and 11, respectively) indicate significant improvements at both daily (p < 0.001) and annual (p < 0.05) time scales in compliance of water management decisions with OBIA guidelines to protect salmon during critical egg-to-fry emergence stages. These FWMT-enabled improvements were achieved without any increased damage to water system infrastructure, riparian property or agricultural production from flood or drought conditions.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".