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Record W2619308922 · doi:10.1186/s13750-017-0093-z

What are the impacts of flow regime changes on fish productivity in temperate regions? A systematic map protocol

2017· article· en· W2619308922 on OpenAlexafffund
Trina Rytwinski, Jessica J. Taylor, Joseph Bennett, Karen E. Smokorowski, Steven J. Cooke

Bibliographic record

VenueEnvironmental Evidence · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans CanadaCarleton University
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaUniversité de MontréalUniversity of Washington
KeywordsProductivityEcosystemSustainabilityEnvironmental resource managementStreamflowEcologyHabitatEnvironmental scienceGeographyNatural resource economicsFisheryBiologyEconomicsDrainage basin

Abstract

fetched live from OpenAlex

Abstract Background Ecosystem changes from altered flows can have multiple impacts on fish, including changes to physical habitat, habitat access, food supplies, behaviour, community composition, energy expenditure, and population dynamics. There is growing evidence of the potential negative consequences of altered flow regimes on fluvial ecosystems and the fisheries they support. As such, the scientific and policy communities have acknowledged the need for maintaining or restoring natural flow variability in order to sustain ecological health of fluvial ecosystems. However, for resource managers, making decisions on the potential effects of flow alterations on fish productivity has been problematic because there are still uncertainties regarding flow-fish productivity relationships. Therefore, to ensure the maintenance of healthy and productive aquatic ecosystems and the sustainability of riverine fisheries, a better understanding of the impacts of flow alteration on fish productivity is needed. Due to the wide scope of this review, and the diversity of fish productivity outcomes used to evaluate flow alteration impacts, the set of studies will be quite heterogeneous. Therefore, prior to undertaking a comprehensive and quantitative synthesis, we propose to begin with a systematic map to provide an overview of the available evidence on the impacts of flow regime changes on fish productivity. We will also use this systematic map to identify subtopics that are sufficiently covered by existing studies to allow full systematic reviewing. Methods This systematic map will compile evidence on the impacts of flow regime changes on fish productivity. All studies that evaluate the effects of flow regime change on direct outcomes of fish productivity, will be included in the review. We will use a broad definition of fish productivity to include any measurement related to: biomass, abundance, density, yield, diversity, growth, survival, individual performance, migration, reproduction, recruitment, or surrogate thereof. Relevant causes of a change in/modification to flow regime can include: (1) anthropogenic causes: dams, reservoirs (impoundments), hydroelectric facilities, locks, levees, water withdrawal (abstraction), water diversion, land-use changes, and road culverts; or (2) natural causes: climate change (possible indirect anthropogenic cause as well), floods, droughts, seasonal changes. Any freshwater or estuarine fish species or species groups in temperate regions will be considered. The review will include a wide range of sources including primary and grey literature and use public databases, search engines and specialist websites. A searchable database containing extracted meta-data from relevant included studies will be developed and provided as a supplementary file to the map report. The final narrative will describe the quantity and key characteristics of the available evidence, identify knowledge gaps for future research and identify subtopics that are sufficiently covered by existing studies to allow full systematic reviewing.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.050
metaresearch head score (Gemma)0.085
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.055
Threshold uncertainty score0.263

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.085
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0080.007
Bibliometrics0.0290.015
Science and technology studies0.0030.002
Scholarly communication0.0040.006
Open science0.0040.005
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0550.007

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 head0.032
GPT teacher head0.272
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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

Citations31
Published2017
Admission routes2
Has abstractyes

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