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Record W4413450232 · doi:10.1139/cjfas-2025-0139

A salmon ecosystem conceptual foundation for estuary restoration

2025· article· en· W4413450232 on OpenAlexvenueno aff
Daniel L. Bottom, Kim K. Jones, Amy B. Borde, Kirk L. Krueger, Gary E. Johnson, Janine M. Castro, Ronald M. Thom

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersBonneville Power Administration
KeywordsEstuaryFoundation (evidence)EcosystemEnvironmental scienceRestoration ecologyFisheryEcologyEnvironmental resource managementGeographyBiology

Abstract

fetched live from OpenAlex

A cohesive recovery strategy for Pacific salmon populations requires a shared conceptual foundation to coordinate management actions across jurisdictions and interests. We examined an estuary restoration program as a case study of a life history ecosystem framework proposed for Columbia River salmon conservation in 1999. The estuary program lacks an explicit framework to account for cross-scale connections to other salmon life stages. We propose the following conceptual foundation, composed of three guiding principles: (1) the estuary is a subsystem of a complex natural-cultural system connected by salmon life cycles; (2) a dynamic mosaic of estuarine habitats supports growth, ontogenetic development, and life history variations of salmon throughout the basin; and (3) physical and biological processes beyond the estuary limit opportunities for salmon life history expression within it. These principles require additional estuary performance indicators to coordinate water and fishery management programs and to adapt estuary restoration actions to a changing climate.

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)0.004
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0040.008
Scholarly communication0.0070.005
Open science0.0030.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.000

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 head0.019
GPT teacher head0.230
Teacher spread0.211 · 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 designTheoretical or conceptual
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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

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