MétaCan
Menu
Back to cohort

Effectiveness monitoring of fish passage facilities: historical trends, geographic patterns and future directions

2009· article· en· W2135045365 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFish and Fisheries · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFish <Actinopterygii>Scope (computer science)Temperate climateLocationGeographyEnvironmental resource managementEcologyBiologyFisheryComputer scienceEnvironmental science

Abstract

fetched live from OpenAlex

Abstract Fishways and other passage facilities frequently prevent or delay the passage of fishes, highlighting the need for effectiveness monitoring. We reviewed the scientific literature from 1960 to 2008 reporting on effectiveness monitoring of fish passage facilities to assess what taxa and life‐stages have been studied, the questions that are asked during evaluation, and how these varied over time or by geographic region. We identified 96 peer‐reviewed articles of which 68% focused on passage by adult fishes. Salmoniformes was the most studied order (58% of studies). The focus of fishway evaluations did not change over the years, but varied significantly by geographic region. Studies from the tropics had a broader taxonomic scope than studies from temperate locations. Exogenous mechanisms of passage failure, such as environmental, structural and behavioural factors, were studied in 90% of studies from North America but only ∼50% of studies from Europe, South America and Australia. Endogenous (i.e. physiological) mechanisms affecting passage success were not often assessed anywhere, though they were a powerful means of evaluating mechanisms of failure. Few studies monitored migration after fish had left a facility. To improve effectiveness monitoring of passage facilities, we suggest that both endogenous and exogenous mechanisms need to be studied in an integrated fashion to understand passage failure and to inform design or operational changes that could improve passage efficiency. In addition post‐departure monitoring is required to more completely assess the fitness consequences of passage.

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.

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 score0.194
Threshold uncertainty score0.471

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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