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Record W2593788219 · doi:10.1139/cjfas-2016-0455

Intensified environmental and density-dependent regulation of white perch recruitment after an ecosystem shift in the Hudson River Estuary

2017· article· en· W2593788219 on OpenAlexvenueno aff
Brian K. Gallagher, David H. Secor

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsnot available
FundersRiver Foundation
KeywordsDreissenaEstuaryPerchDensity dependenceBiologyZebra musselFisheryEcologyYolkBivalviaMolluscaFish <Actinopterygii>MusselPopulation

Abstract

fetched live from OpenAlex

Long-term monitoring data were used to test whether the invasion of zebra mussels (Dreissena polymorpha) in the Hudson River Estuary in 1991 altered the influence of density dependence and environmental conditions on life-stage transitions, growth, and partial migration in white perch (Morone americana). During the post-invasion period (1992–2013), we estimated standing stocks of white perch eggs, yolk-sac larvae (YSL), post-yolk-sac larvae (PYSL), young-of-the-year (YOY), and adults as well as indices of YOY growth and spatial distribution. A series of linear and nonlinear functions were employed to model life-stage transitions, while the effects of six environmental and density-dependent variables on YOY growth and partial migration were quantified. Comparisons to pre-invasion observations (1974–1991) indicated that egg–YSL, PYSL–YOY, and YOY–yearling transitions changed significantly after the invasion, while PYSL abundance developed a stronger negative effect on YOY growth. The PYSL–YOY transition became more sensitive to density dependence and freshwater flow from 1992 to 2013, which is consistent with diminished abundance and increased environmental sensitivity of the forage base in the Hudson River Estuary reported after the zebra mussel invasion.

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 score0.072
Threshold uncertainty score0.144

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 studies0.0000.000
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 head0.037
GPT teacher head0.239
Teacher spread0.202 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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