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Record W3200570328 · doi:10.1071/mf20377

Ecohydraulic model for designing environmental flows supports recovery of imperilled Murray cod (Maccullochella peelii) in the Lower Darling–Baaka River following catastrophic fish kills

2021· article· en· W3200570328 on OpenAlexaff
Ivor Stuart, Clayton Sharpe

Bibliographic record

VenueMarine and Freshwater Research · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsRealNetworks (Canada)
FundersNSW Department of Primary IndustriesNSW Department of Planning,Industry and EnvironmentWaterNSW
KeywordsEcosystemEnvironmental scienceFisheryRiver ecosystemAridWater qualityFreshwater ecosystemHydrology (agriculture)Fish migrationEcologyBiologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

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. &amp;gt;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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.269
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2021
Admission routes1
Has abstractyes

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