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Record W2973625281 · doi:10.1139/cjfas-2018-0434

Effects of urbanization of coastal watersheds on growth and condition of juvenile alewives in New England

2019· article· en· W2973625281 on OpenAlexvenueno aff
Rita Monteiro Pierce, Karin E. Limburg, Daniella Hanacek, Iván Valiela

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersNational Ocean ServiceNational Oceanic and Atmospheric AdministrationState of Maine Department of Marine Resources
KeywordsAlewifeUrbanizationWatershedTrophic levelEcologyEnvironmental scienceFisheryLand coverGeographyLand useBiologyPredation

Abstract

fetched live from OpenAlex

Alosa pseudoharengus (alewife) has declined throughout New England. A factor that may be responsible for such stock reductions is urbanization of watersheds discharging into alewife nursery ponds. We found that young-of-the-year (YOY) alewife length, weight, condition factor, and growth rate decreased in relation to increased urban land cover on watersheds of nine Massachusetts and Maine ponds. The watersheds ranged from 3% to 60% urbanized land cover. YOY δ15N increased significantly in proportion to urbanized land cover on watersheds, suggesting a concrete link between watershed land cover and YOY alewife metrics, which is in agreement with previous knowledge that N discharges from more urbanized watersheds bear higher δ15N. The New England results confirmed results across a wide latitudinal gradient that suggested that the size of YOY alewife decreased as urban land cover on watersheds increased. The dominant influence of urban land cover in the YOY is highlighted by the fact that YOY alewife from ponds with the highest percentage of urban cover reached δ15N as high as that of adult spawners migrating from the ocean, who feed at higher trophic levels.

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 score0.159
Threshold uncertainty score0.317

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.005
GPT teacher head0.175
Teacher spread0.171 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

Explore more

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