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Record W4396646688 · doi:10.1007/s12237-024-01355-6

Local Watershed Properties Cannot Explain Divergent Dynamics of Pacific Herring in an Urbanizing Estuary

2024· article· en· W4396646688 on OpenAlexaff
Fabienne Urfer, Timothy E. Essington, Blake E. Feist, Tessa B. Francis

Bibliographic record

VenueEstuaries and Coasts · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsSimon Fraser University
FundersCalifornia Department of Fish and WildlifeWashington Sea Grant, University of WashingtonWashington State University
KeywordsPacific herringImpervious surfaceWatershedEstuaryLand coverEnvironmental scienceStock (firearms)GeographyLand useSound (geography)PopulationFisheryClupeaEcologyHerringOceanographyBiologyGeology

Abstract

fetched live from OpenAlex

Abstract Pacific herring ( Clupea pallasii ) is a foundational species in Puget Sound (Washington State, U.S.A.) and is subject to many anthropogenic threats. We assessed the overall status of the Puget Sound Pacific herring sub-stock complex and asked whether watersheds with less urban or agricultural land cover, less impervious surface, and lower human density were associated with better stock status. To this end, we developed multiple metrics of sub-stock population status; characterized watershed properties with respect to land use/land cover, percent impervious surfaces, and human density; and used statistical model selection to evaluate the weight of evidence in support of our hypotheses. Overall, the status of sub-stocks was poor; metrics for most sub-stocks indicate a decline from 1996–2021. However, the status metrics of sub-stocks were not related to recent (2016) watershed characteristics or the rate of change in watershed characteristics from the mid-1990s to 2016. While the cumulative effects of local human land use throughout Puget Sound may be contributing to the deterioration of spawning biomass, these results also suggest that other drivers that operate at larger scales (e.g., predation, disease, climate) are likely important.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.999

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 studies0.0000.000
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 head0.017
GPT teacher head0.220
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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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