Northwest Calvert sea wrack temporal data, Central Coast, British Columbia (2016-2017)
Bibliographic record
Abstract
This dataset contains the collection site locations, visit dates, and weights of each species of shore cast algae or seagrass found during each site visit. Dead, shore-cast seaweeds and seagrasses (commonly called sea wrack) provide an important vector of marine-derived nutrients to low productivity terrestrial environments, such as beaches. However, little is known about the processes that facilitate wrack transport, deposition, and accumulation in coastal temperate British Columbia. Throughout the course of one year, I visited three sites on a bi-monthly basis to document temporal changes in wrack biomass and species composition. At each site, wrack was measured along 12 permanent 1-meter wide belt-transects. The transects ran perpendicular to the water's edge, originating at the forest-beach interface and extending to the ocean at low tide. I found wrack biomass to be similar throughout all four seasons, wrack species composition, however, varied. My results suggest sea wrack is a consistent vector of potential nutrients from the marine to the terrestrial environment in British Columbia. Detailed methods and conclusions in the MSc thesis found in the linked folder. Sara Wickham – University of Victoria, Brian Starzomski – University of Victoria; John Reynolds – Simon Fraser University; Chris Darimont – University of Victoria
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.027 |
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".