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Record W7034165963

Spatial and temporal patterns of biofouling aquatic invasive species in the Salish Sea

2022· article· en· W7034165963 on OpenAlexaboutno aff

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2022
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsBiofoulingInvasive speciesRecreational fishingWatercraftTunicateAquatic animalIntroduced speciesSpatial ecology
DOInot available

Abstract

fetched live from OpenAlex

Aquatic invasive species (AIS) have the potential to result in significant ecological and economic impacts. In the Salish Sea a number of primary and secondary invasion vectors exist including commercial shipping, recreational boating, and aquaculture-related movements that can introduce or spread AIS. An important subset of all AIS in this area are biofouling species, including tunicates and bryozoans, that can outcompete and displace native species. To better understand spatial and temporal patterns of biofouling AIS in the Salish Sea, Fisheries and Oceans Canada (DFO) has carried out a monitoring program since 2006 – a program that has expanded in scope and scale as partnerships with First Nations, stakeholders, and industry have developed. Here we report on the detection and spread of biofouling AIS, including results from the 2021 field season. To date, this program has identified over a dozen biofouling AIS in the Salish Sea. There has been substantial “localized” spread of many common biofouling AIS over time, particularly for colonial tunicate species, a pattern that isn’t explained by sampling effort alone. Thus, given the life histories of these species, the movement of small vessels and other floating infrastructure is likely a crucial vector facilitating their spread throughout the Salish Sea.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.235
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.221
Teacher spread0.198 · 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 teacher head, 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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