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Record W3205573648 · doi:10.31542/r.gm:3053

Assessing the spread and establishment of Prussian carp (Carassius gibelio) in northern Alberta

2021· dissertation· en· W3205573648 on OpenAlexaffabout
Erika Jessen

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsMacEwan University
Fundersnot available
KeywordsCarassiusInvasive speciesFisheryIntroduced speciesGeographyCarpFreshwater fishPrussian blueHabitatEcologyFish <Actinopterygii>Biology

Abstract

fetched live from OpenAlex

The Prussian carp (Cassasius gibelio) is an exceptionally dangerous invasive freshwater fish species. Native to Asia and eastern Europe, it has come to dominate many freshwater bodies across Eurasia through anthropogenic activities, causing extensive ecological damage by outcompeting native taxa and degrading environmental conditions. Within the last two decades, the Prussian carp has been introduced into Alberta, and has since spread into the rivers and lakes of the province. To date, most research relating to Prussian carp in North America has focused exclusively on southern Alberta. My research project aimed to expand research into northern Alberta, specifically the Edmonton region, with the objective to determine if Prussian Carp have spread into northern Alberta. Twelve lakes and ponds in the Edmonton area were surveyed using an underwater drone to collect footage. Four of these sites were further subjected to eDNA analysis. The results of the drone footage picked up a mixture of native and invasive fish species, with two being positive for goldfish. The eDNA analysis picked up neither goldfish or Prussian carp DNA at any of the test sites, likely due to low eDNA concentrations. Overall, these results highlight the need for ecological management to mitigate the spread of invasive fish species in Alberta.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.111
Threshold uncertainty score0.224

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
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 head0.010
GPT teacher head0.243
Teacher spread0.232 · 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
Published2021
Admission routes2
Has abstractyes

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