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Record W2137032257 · doi:10.1071/mf11128

Constraints on the recovery of invertebrate assemblages in a regulated snowmelt river during a tributary-sourced environmental flow regime

2011· article· en· W2137032257 on OpenAlexaff
Andrew J. Brooks, Matthew Russell, Robyn Bevitt, Matthew Dasey

Bibliographic record

VenueMarine and Freshwater Research · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicFreshwater macroinvertebrate diversity and ecology
Canadian institutionsDepartment of Environment and Conservation
Fundersnot available
KeywordsBiotaTributaryInvertebrateBiological dispersalEcologySnowmeltEnvironmental scienceHabitatBiologyGeography

Abstract

fetched live from OpenAlex

The impacts of river regulation on aquatic biota have been extensively studied, but long-term assessments of the restoration of biota by environmental flows and the principal mechanisms of recovery have rarely occurred. We assessed whether the provision of an environmental flow regime (EFR) via the decommissioning of an aqueduct on a tributary stream altered downstream macroinvertebrate assemblages in the highly regulated Snowy River, Australia. Macroinvertebrate assemblages of the Snowy River, reference and control sites remained distinct despite the provision of environmental flows. Invertebrate assemblages detrimentally affected by regulation probably remained impaired due to either constraints on colonisation from the tributary stream (dispersal constraints) or unsuitable local environmental conditions in the Snowy River caused by flow regulation (e.g. high levels of fine sediments, elevated temperature regime) suppressing new colonists or recovery of extant populations. Our study showed that restoration may be ineffective if EFRs are too small to ameliorate local environmental factors constraining the recovery of affected biota. Other barriers to recovery, such as dispersal constraints, also need to be overcome. Successful restoration of regulated rivers using environmental flows requires an understanding of the mechanisms and pathways of recovery, together with identification and amelioration of any potential barriers to recovery.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.979

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0220.001

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.

Opus teacher head0.039
GPT teacher head0.223
Teacher spread0.184 · 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.

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

Citations21
Published2011
Admission routes1
Has abstractyes

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