Early detection monitoring sites for European green crab in the Salish Sea
Bibliographic record
Abstract
European Green Crab (EGC; Carcinus maenas) was first detected in Sooke Basin in 2012, and in US waters of the Salish Sea in 2016. EGC is not yet known to be established in Canadian waters of the Salish Sea outside of Sooke, presenting an opportunity for early detection and management of newly invaded areas. AIS managers require information on where to target early detection monitoring programs. As such five existing models were evaluated based on habitat suitability using 447 sites in the Salish Sea. Each of the five individual models was informative at identifying sites suitable for EGC in Canadian waters of the Salish Sea but there was generally low agreement among models, likely because each model incorporates different aspects of EGC biology and habitat use. Without an independent validation dataset it was not possible to identify a single “best” model to identify early detection sites. An ensemble model approach can buffer uncertainty arising from individual models by combining the outputs of multiple individual models. To incorporate all of the models in an ensemble, the outputs for each model were rank-transformed into 20th percentiles (i.e., rescaled values 1-5), and the ‘mode’ (most frequent) value across all five models was determined for each site (n = 447). The 68 sites with a rank-transformed mode of 5 were prioritized for early detection monitoring (see Figure 1). While the 68 sites identified can serve as a starting point, there are other considerations beyond habitat suitability when selecting specific monitoring sites for EGC in the Salish Sea that were beyond the scope of this process. Managers may choose to add or remove sites as needed based on likelihood of arrival (e.g., through larval drift, human-mediated movements, immigration); presence of other important features (e.g., eelgrass); presence of available prey/absence of predators; ecologically, economically, or culturally important areas; site access; or partner interest. The ensemble model approach developed here may be applied elsewhere by using or deriving models specific for that region, based on the available data and management objectives. However, the input data does not capture all factors, including propagule pressure, that may contribute to invasion success.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".