Mussel Restoration Across an Estuarine Environmental Gradient: Implications for Site Selection
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".