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Record W4417397416 · doi:10.3390/fishes10120653

Mussel Restoration Across an Estuarine Environmental Gradient: Implications for Site Selection

2025· article· en· W4417397416 on OpenAlexaff
Peter van Kampen, Andrew Jeffs, Shane Kelly, Mark Wilcox

Bibliographic record

VenueFishes · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Bivalve and Aquaculture Studies
Canadian institutionsFisheries and Oceans Canada
FundersUniversity of Auckland
KeywordsEstuaryMusselHabitatPredationMolluscaCondition indexBivalviaTransplantation

Abstract

fetched live from OpenAlex

The transplanting of juvenile and adult mussels onto soft sediments is an emerging technique for the ecological restoration of the biogenic habitat formed by mussels. While these habitats are often found within estuarine systems, the spatial suitability of these environments for restoration is poorly described. The dynamic and variable environmental conditions characteristic of estuaries could represent challenges to the persistence of restored mussel beds. To assess whether there are spatial differences in mussel responses to transplantation within an estuarine environment, six experimental mussel beds of adult green-lipped mussels (Perna canaliculus) were established along an environmental gradient in a small estuarine harbour in northern New Zealand. Transplanted mussel beds were sampled immediately after installation and again at 3 and 9 months later. Minor differences in the density, length and condition index of mussels were identified among the six sites over the course of the study; however, their responses were typically similar across sites. These results suggest that these mussels have the capacity to establish themselves within estuarine environments and that their subsequent performance once transplanted onto the seafloor appears to be determined by other site-specific factors, such as the presence of predators and the degree of exposure to storm waves.

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 score0.142
Threshold uncertainty score0.367

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 head0.013
GPT teacher head0.281
Teacher spread0.268 · 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
Published2025
Admission routes1
Has abstractyes

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