Ecohydraulic model for designing environmental flows supports recovery of imperilled Murray cod (Maccullochella peelii) in the Lower Darling–Baaka River following catastrophic fish kills
Bibliographic record
Abstract
Large dryland and semi-arid rivers are among the world’s most heavily modified ecosystems, and the Darling–Baaka River of eastern Australia highlights the challenges in conserving such ecosystems. Since 2000, the hydrology at the downstream end of the system (the Lower Darling River, LDR) has been transformed from a naturally near-perennial flowing system to an intermittent one by increased water abstraction, prolonged drought and climate change. This hydrological change has placed immense pressure on the native fish populations, such as the imperilled Murray cod (Maccullochella peelii), as evidenced by the 2018–19 catastrophic fish kills. Here we outline an ecohydraulic conceptual model for designing environmental flows to support spawning and recruitment of Murray cod. An environmental flow based on this model was released in 2016–17, following 524 consecutive days of continuous zero flows. The LDR flow consisted of an increased discharge in late winter–spring to promote broad-scale lotic (i.e. >0.3 m s–1) conditions, hydraulic complexity and continuous base flows to maintain connectivity and water quality. Monitoring of Murray cod during and following the flow revealed successful spawning and recruitment. This finding is significant because it provides justification for altering current water management policies that are failing to protect this nationally significant ecosystem.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".