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Record W3003538079 · doi:10.1111/ecog.04476

Shellfish subsidies along the Pacific coast of North America

2020· article· en· W3003538079 on OpenAlexafffund
Kieran Cox, Hailey L. Davies, Katie Davidson, Travis G. Gerwing, Sarah E. Dudas, Francis Juanes

Bibliographic record

VenueEcography · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsFisheries and Oceans CanadaASL Environmental Sciences (Canada)Tula FoundationUniversity of Northern British ColumbiaUniversity of Victoria
FundersHakai InstituteNatural Sciences and Engineering Research Council of CanadaLiber Ero FoundationPADI Foundation
KeywordsShellfishTrophic levelEcologyEcosystemMarine ecosystemSubsidyFisheryTemperate climateProductivityGeographyMarine conservationInvertebrateOceanographyEnvironmental scienceBiologyAquatic animalFish <Actinopterygii>Geology

Abstract

fetched live from OpenAlex

Spatial subsidies are associated with pronounced ecosystem responses, as nutrients cross ecological boundaries and cascade through food webs. While the importance of subsidies is known, the role of shellfish, specifically molluscs, as a marine subsidy has not been formally described. Focusing primarily on the Pacific coast of North America, we identify vectors that transport shellfish‐derived nutrients into coastal terrestrial environments, including birds, mammals, and over 13 000 yr of marine resource use by people. Evidence from recipient ecosystems suggests shellfish drastically influence soil chemistry, forest productivity and the diversity of primary producers at the regional and landscape level. Responses in higher trophic levels have not yet been investigated, but given documented responses in lower trophic levels, this may be due to a lack of examination. To determine if the processes we describe within the northeast Pacific are pertinent to coastal environments worldwide, we also explore shellfish subsidies globally, with a specific focus on temperate and tropical islands. As shellfish are not as spatially or temporally constrained as other subsidies, our examination suggests our findings are applicable to many other geographical regions along the marine–terrestrial interface.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
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
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.187
Teacher spread0.179 · 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.

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

Citations15
Published2020
Admission routes2
Has abstractyes

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