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Record W4283027438 · doi:10.1002/fsh.10802

Managing Small Fish at Large Scales: The Emergence of Regional Policies for River Herring in the Eastern United States

2022· article· en· W4283027438 on OpenAlexaboutno aff
Jacob P. Kritzer, Carolyn Hall, Bruce Hoppe, Curtis Ogden, Jamie Cournane

Bibliographic record

VenueFisheries · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHerringFisheryFish <Actinopterygii>GeographyBiology

Abstract

fetched live from OpenAlex

Abstract Anadromous Alewives Alosa pseudoharengus and Blueback Herring A. aestivalis, collectively known as “river herring,” provide ecosystem services to coastal communities in the Eastern United States. Despite traditions of community-based stewardship, many populations exhibit long-term declines. Their name notwithstanding, river herring spend most of their lives at sea and stray among natal rivers. Therefore, absence of management above individual rivers can compromise population viability, local conservation investments, and ecosystem services. Large-scale management in the USA was achieved for the first time during 2007–2015 by amending three Fishery Management Plans and creating a broader River Herring Conservation Plan following an Endangered Species Act petition. Concurrently, an international restoration plan for river herring in the St. Croix River on the USA–Canada border was adopted. A survey of stakeholder organizations in New England during this period revealed widespread concern for stressors managed at different scales, but that most action was directed locally. Stakeholder collaboration networks were clustered within states and around loose regional hubs, matching the scales of stakeholder concerns and actions. Unfortunately, river herring face growing threats linked to climate change, effects of which will be felt at local and regional scales, while effective mitigation will require actions at national and global scales.

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 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 score0.079
Threshold uncertainty score0.934

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 studies0.0010.000
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 head0.025
GPT teacher head0.224
Teacher spread0.199 · 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 teacher head, 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

Citations6
Published2022
Admission routes1
Has abstractyes

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