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Record W2300788015 · doi:10.1111/fme.12178

Burbot and large hydropower in North America: benefits, threats and research needs for mitigation

2016· article· en· W2300788015 on OpenAlexafffund
Philip M. Harrison, Lee F.G. Gutowsky, Eduardo G. Martins, David A. Patterson, Steven J. Cooke, Michael Power

Bibliographic record

VenueFisheries Management and Ecology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsSimon Fraser UniversityFisheries and Oceans CanadaCarleton UniversityUniversity of Waterloo
FundersFisheries and Oceans CanadaBC HydroNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsHydropowerFisheryVulnerability (computing)Endangered speciesEnvironmental sciencePsychological resilienceGeographyHabitatEcologyBiology

Abstract

fetched live from OpenAlex

Abstract In North America, burbot Lota lota (L.), interactions with hydropower are common, southern burbot populations are endangered and hydropower has been implicated in the decline. Thus, the objectives of this review were to identify threats and benefits of hydropower to riverine and reservoir dwelling burbot, assess overall impacts and identify key research needs for mitigation. Review findings suggest that while winter hydropower discharge regimes threaten riverine burbot spawning, burbot often successfully exploit reservoir environments. Further, while turbine entrainment has been hypothesised as a factor in the decline of burbot populations, low adult vulnerability to passage and a high resilience to juvenile removals likely mean risk to populations is low. Identified research needs include better definitions of flow and temperature requirements for riverine burbot and a quantification of the potential for reservoirs to act as source populations for downstream impacted zones. This review highlights the Pacific watershed focus of existing work and calls for status assessments and ecological research of burbot in North American Arctic and Atlantic watersheds where large hydropower and burbot interactions are common yet rarely studied.

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)0.000
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 score0.096
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.246
Teacher spread0.228 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

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