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Record W3126916776 · doi:10.1002/tafs.10292

Recovery of Diadromous Fishes: A Kennebec River Case Study

2021· article· en· W3126916776 on OpenAlexaboutno aff
Gail S. Wippelhauser

Bibliographic record

VenueTransactions of the American Fisheries Society · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAlewifeAlosaFish migrationFisheryDam removalHydropowerHabitatFish <Actinopterygii>HerringGizzard shadDorosomaEnvironmental scienceBiologyEcologySediment

Abstract

fetched live from OpenAlex

Abstract The lack of safe, timely, and effective fish passage at dams remains a major impediment to the restoration of diadromous fish species to their historic range and abundance. Because fish passage facilities are often ineffective, dam removal has increasingly been used to restore diadromous species. However, few quantitative studies have assessed the response of fishes to dam removal. This paper documents the long‐term response of multiple fish species in the Kennebec River, Maine, to the removal of two hydropower dams and the installation of upstream fish passage facilities that restored access to historic habitat. Edwards Dam was removed in 1999, and Fort Halifax Dam was removed in 2008. Counts of river herring (Alewife Alosa pseudoharengus and Blueback Herring A. aestivalis), American Shad A. sapidissima, and Striped Bass Morone saxatilis at upstream dams confirmed that these species quickly recolonized the 27‐km, free‐flowing segment of the Kennebec River that became accessible after Edwards Dam was removed. Average counts of river herring increased by 228% after the removal of Edwards Dam and by 1,425% after the removal of Fort Halifax Dam. However, access to a substantial amount of habitat in the Kennebec River is blocked by ineffective passage at the lowermost dam.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.218
Threshold uncertainty score0.433

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.011
GPT teacher head0.216
Teacher spread0.205 · 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 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

Citations22
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the American Fisheries SocietySame topicFish Ecology and Management StudiesFrench-language works237,207