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Record W2886764536 · doi:10.1139/cjfas-2018-0188

Productivity of riverine habitats may be changing for American eel

2018· article· en· W2886764536 on OpenAlexaffvenueabout
Heather D. Bowlby

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsAnguilla rostrataProductivityFisheryHabitatEscapementAbundance (ecology)PopulationJuvenileNova scotiaGeographyEstuaryEcologyBiology

Abstract

fetched live from OpenAlex

The panmictic population of American eel (Anguilla rostrata) is at risk, making any region that supports enhanced production important from a recovery perspective. Strong glass eel runs to a small number of rivers along the Atlantic coast of Nova Scotia are thought to indicate high productivity, partially buffering declines occurring in other regions. However, contrary to glass eel indices of recruitment, an index representing older juveniles has strongly declined in riverine habitats throughout Nova Scotia from 1995 to 2005, with evidence of substantial differences in relative abundance among watersheds. This suggests that glass eel indices may not reflect trends of older juveniles and consequently that the contribution of Atlantic coast rivers to population persistence may be overstated. More recent monitoring from two rivers shows divergent trends in juvenile eel abundance, underscoring the importance of widespread surveys to assess changes in regional productivity. Further evaluation of the watershed characteristics associated with higher juvenile abundance would aid in understanding differences in productivity among watersheds and possibly in facilitating increased spawning escapement for American eel.

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)0.000
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 score0.116
Threshold uncertainty score0.230

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.235
Teacher spread0.214 · 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

Citations5
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic Sciences→Same topicFish Ecology and Management Studies→French-language works237,207→