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Record W7133273639

National screening-level risk assessment (SLRA) of Goldfish, Prussian Carp, Chain Pickerel, and Black Crappie in Canada

2025· other· en· W7133273639 on OpenAlexaboutno aff
J. M. Hill, M. Simard, A. M. Weise, J. Hubbard, S. Kingsbury

Bibliographic record

VenueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du Canada · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsRisk assessmentEcoregionBiological dispersalInvasive speciesRange (aeronautics)PopulationIntroduced species
DOInot available

Abstract

fetched live from OpenAlex

Aquatic invasive species (AIS) are species introduced or spread to ecosystems beyond their natural range that threaten biodiversity, economy, and society. Recently, four freshwater fishes were identified as being of concern for Canadian freshwaters: Goldfish (Carassius auratus), Prussian Carp (Carassius gibelio), Black Crappie (Pomoxis nigromaculatus) and Chain Pickerel (Esox niger). The latter two species are native in some regions of Canada, while the carps are strictly non-indigenous. All four species currently have at least one non-indigenous population established in Canada and are undergoing range expansions. A screening-level risk assessment (SLRA) was performed using an adaptation of the Canadian Marine Invasive Screening Tool (CMIST) to identify the level of risk (high, moderate, or low) of these four species across freshwater ecoregions in Canada. SLRAs help decision-makers identify which species pose substantial threats to native species/ecosystems and which may consequently require detailed-level risk assessments. Of the four species assessed, Goldfish presented the highest invasion risk across Canada, especially in southern ecoregions. Prussian carp was a high-risk species in ecoregions within Western Canada and invasion risk of both carps was moderate in all remaining ecoregions. Chain Pickerel was of high risk in New Brunswick and Nova Scotia (and moderate elsewhere, but low in the Arctic), while Black Crappie was of moderate risk everywhere except the Arctic (low risk). High frequency of arrival and a strong potential for dispersal via anthropogenic mechanisms can be correlated to the presence of established AIS populations within an ecoregion (or adjacent ecoregions), because established AIS populations increase future introduction potential. These three correlated factors were the primary likelihood of invasion drivers underpinning risk predictions in this work. Evidence in the literature of impacts on populations, communities, ecosystem functioning, and habitat in their native or invaded range were the primary impact of invasion factors which contributed to elevated final risk levels. A lack of information on the impacts of Black Crappie invasion lowered its risk levels and associated certainty, showing that the availability of information (e.g., on impacts and dispersal mechanisms) played an important role in final determinations of risk. For all four fishes across Canadian ecoregions, some uncertainty in the data means that assessments of moderate risk may reflect the middle point of two potential extremes (i.e. high and low), but true risk may be anywhere from high to low. Low risk is unlikely to be no risk, but nor is it likely to be high risk. Consequently, heatmaps showing risk levels should be used in conjunction with biplots of likelihood and impact scores in order to understand the range in scoring and the certainty associated with likelihood and impact of invasion scores underpinning risk for each fish in each ecoregion. High uncertainty around anthropogenic introductions and activities and the spatial scales over which they operate contribute to greater uncertainty of the risk level for all fishes. Detailed-level risk assessments at finer spatial scales may be pertinent to complete for each species and ecoregion identified here as at high risk for invasion.

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)0.003
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 score0.034
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.012
GPT teacher head0.250
Teacher spread0.238 · 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
Published2025
Admission routes1
Has abstractyes

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Same venueFederal Open Science Repository of Canada / Le Dépôt fédéral de science ouverte du CanadaFrench-language works237,207