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Record W7133273030

Early detection monitoring sites for European green crab in the Salish Sea

2022· other· en· W7133273030 on OpenAlexaboutno aff
Fisheries and Oceans Canada, Pêches et Océans Canada

Bibliographic record

VenueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatStructural basinCritical habitatMode (computer interface)Percentile
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.686

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.239
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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