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Record W2625534365 · doi:10.1002/aqc.2876

Settlement of <scp><i>Ostrea edulis</i></scp> is determined by the availability of hard substrata rather than by its nature: Implications for stock recovery and restoration of the European oyster

2018· article· en· W2625534365 on OpenAlexfundno aff
David Smyth, Anne Marie Mahon, Dai Roberts, Louise Kregting

Bibliographic record

VenueAquatic Conservation Marine and Freshwater Ecosystems · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsOstrea edulisMytilusOysterBenthic zoneHabitatMercenariaFisheryStock (firearms)BiologyBivalviaEcologySubstrate (aquarium)MolluscaGeographyArchaeology

Abstract

fetched live from OpenAlex

Abstract Since the collapse of the Ostrea edulis stock in the mid‐1800s the oyster has struggled to re‐establish itself in self‐sustaining assemblages in Europe. It is now widely recognized that O. edulis is an integral component of a healthy biologically functional benthic environment and, as such, the restoration of wild stocks has become a matter of urgency. A major limiting factor in O. edulis stock recovery is the availability of suitable substrate material for oyster larvae settlement. This research re‐examined the larval settlement potential of several naturally occurring in‐situ shell materials (e.g. Mytilus edulis , Modiolus modiolus , O. edulis ), with the aim of determining which shell material is the most appropriate for large‐scale restoration projects. A positive correlation between available shell material and settlement was determined, and analysis using permanova did not identify an attachment preference by O. edulis to any particular shell type. The findings suggest that if restoration efforts were coordinated with applied hydrodynamic and habitat suitability modelling, in conjunction with naturally occurring shell substrate concentrations, a cost‐effective recovery for O. edulis assemblages in the wild could be achieved.

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)0.001
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 score0.123
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.232
Teacher spread0.215 · 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

Citations47
Published2018
Admission routes1
Has abstractyes

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