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Record W4400045903 · doi:10.1038/s43247-024-01515-3

Ancestral and contemporary intertidal mariculture practices support marine biodiversity in the northeast Pacific

2024· article· en· W4400045903 on OpenAlexafffundabout
Kieran Cox, Hailey L. Davies, Ben Millard‐Martin, Morgan Black, Margot Hessing‐Lewis, Nicole Smith, Francis Juanes, Sarah E. Dudas

Bibliographic record

VenueCommunications Earth & Environment · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsFisheries and Oceans CanadaTula FoundationUniversity of Victoria
FundersPADI FoundationHakai InstituteBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaLiber Ero FoundationCanada Research Chairs
KeywordsMaricultureIntertidal zoneMarine biodiversityBiodiversityFisheryGeographyOceanographyAquacultureEcologyBiologyFish <Actinopterygii>Geology

Abstract

fetched live from OpenAlex

Abstract The cultivation of seafood now outpaces extraction from wild populations. This novel state in the history of human-marine ecosystem interactions highlights the importance of identifying cultivation practices that balance production and conservation. Here, we test the hypothesis that two shellfish cultivation practices, one that spans decades and the other millennia, bolsters habitat complexity, which increases epifaunal (surface) invertebrate diversity. To do so, we conducted multiple surveys of 24 First Nations clam gardens, shellfish aquaculture farms, and non-walled or unmodified beaches along the west coast of Canada. We show that habitat alterations specific to each cultivation practice restructure epifaunal communities at several ecological and spatial scales. Distinct communities within clam gardens and shellfish farms are a function of habitat complexity at 25–50 and 50–100 cm resolutions and changes in the amount of gravel, bivalve shells, and seaweeds. Our findings highlight how resource cultivation can contribute to achieving sustainable human-ecosystem interactions.

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 score0.481
Threshold uncertainty score0.892

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.041
GPT teacher head0.264
Teacher spread0.223 · 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

Citations3
Published2024
Admission routes3
Has abstractyes

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