Assembling a multi species view of population level differentiation of marine life in the Salish Sea.
Bibliographic record
Abstract
Our goal is to understand the Salish Sea ecosystem more completely by studying the population genetics of multiple species, plants and animals, covering a variety of habitats, trophic, and taxonomic groups. We believe these data will help inform ecosystem based management, e.g., the practical design of a network of marine protected areas or reservoirs (MPA). We present results of a population genetic study of English sole collected within the Salish Sea and from offshore as far north as Haida Gwaii, a study done in cooperation with NOAA Teacher in the Laboratory, Canadian Department of Fisheries and Oceans (CDFO), and Washington Department of Fish and Wildlife (WDFW). We also illustrate a sample design and the cooperative nature of this type of work by presenting the status of a new genetic and phenetic study of spot prawn that has been initiated with funds from The Suquamish Tribe and in cooperation with CDFO; WDFW; The Lummi, Muckleshoot, Nisqually, and Swinomish tribes; Vancouver Aquarium; and Mariner High School, Everett, WA. Patterns of population variability demonstrated from work in other research laboratories for Olympia oysters, Pacific herring and hake, and yellow eye rockfish are discussed. The ecosystem is experiencing change, including climate change, ocean acidification, and urban growth, and identifying the geographic nature of population variability for a variety of species will help us establish better tools for monitoring and protecting species, species groups, and habitats. In the process, we will learn what abiotic and biotic factors affect the patterns genetic connectivity for different marine taxonomic groups, and therefore be in a better position to understand, model, and track population responses to environmental change. Collaborative and cooperative work like this highlights the diversity of animal and plant life that is found but rarely recognized in our marine backyards.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".