Sea wrack delivery and accumulation on islands: factors that mediate marine nutrient permeability
Bibliographic record
Abstract
Sea wrack provides an important vector of marine-derived nutrients to many terrestrial environments. However, little is known about the processes that facilitate wrack transport, deposition, and accumulation on islands. Three broad factors can affect the stock of wrack along shorelines: the amount of potential donor habitat nearby, climatic events that dislodge seaweeds and transfer them ashore, and physical characteristics of shorelines that retain wrack at a site. To determine when, where, and how wrack accumulates on island shorelines, we surveyed 455 sites across 101 islands in coastal British Columbia, Canada. At each site, we recorded wrack biomass, species composition, and shoreline biogeographical characteristics. Additionally, over a period of 9 mo, we visited a smaller selection of sites (n = 3) every 2 mo to document temporal changes in wrack biomass and species composition. Dominant wrack species were Zostera marina, Fucus distichus, Macrocystis pyrifera, Nereocystis luetkeana, Pterygophora californica , and Phyllospadix spp. The amount of donor habitat positively affected the presence of accumulated biomass of sea wrack, whereas rocky substrates and shoreline slope negatively affected the presence of sea wrack biomass. Biomass was higher during winter months, and species diversity was higher during summer months. These results suggest that shorelines with specific characteristics have the capacity to accumulate wrack, thereby facilitating the transfer of marine-derived nutrients to the terrestrial environment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".