Species Checklists for Salish Sea Seaweeds
Bibliographic record
Abstract
There are nearly 700 seaweed species, subspecies and varieties reported in the Northeast Pacific, many with a distribution that includes British Columbia. Yet, it can be difficult to access reported distribution information at a regional scale; for example: does a seaweed species reported from Southern B.C. include the Salish Sea or just the West coast of Vancouver Island? Occurrence data for many seaweeds exist in the form of herbarium specimens, DNA records, and observations made by governmental agencies and citizen science initiatives. However, there are challenges to building species lists from these data, including: difficulty collating the disparate data types into regionally-specific lists, especially for data that are not accessible through the Global Biodiversity Information Facility (GBIF); low reliability of records for certain taxonomic groups due to incorrect identifications or frequent taxonomic reclassifications (both common issues for seaweeds); and infrequent or geographically-biased sampling efforts. An example of the last challenge: the last comprehensive floristic seaweed survey around Greater Vancouver was in 1983 by researchers at the University of British Columbia, the results of which are unpublished. The lack of local species checklists for ecologically-important marine organisms like seaweeds is surprising given that these checklists can aid in management and Greater Vancouver (and the Salish Sea in general) are experiencing ever-increasing human impacts. The goals of this project were to 1) update the checklist of seaweeds for Greater Vancouver using intertidal surveys, and 2) create a checklist of seaweed species for regions of the Salish Sea, specifically the Strait of Georgia and Juan de Fuca Strait, by collating occurrence data from herbaria, DNA barcoding records, and verified citizen science records. These checklists provide insight into seaweed richness within specific areas of the Salish Sea. Finally, we make recommendations based on biases and gaps in historic sampling efforts.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.019 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".