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Record W4406312903 · doi:10.1080/10402381.2024.2446911

Impacts of aquatic thrusters on submerged aquatic plants in Kawartha Lakes, Ontario, Canada

2025· article· en· W4406312903 on OpenAlexaffabout
Brett Tregunno, Tanner Liang

Bibliographic record

VenueLake and Reservoir Management · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsToronto and Region Conservation Authority
Fundersnot available
KeywordsAquatic plantEnvironmental scienceAquatic ecosystemAquatic environmentFisheryHydrology (agriculture)OceanographyEcologyBiologyGeologyMacrophyte

Abstract

fetched live from OpenAlex

Tregunno B, Liang T. 2025. Impacts of aquatic thrusters on submerged aquatic plants in Kawartha Lakes, Ontario, Canada. Lake Reserv. Manage. 41:34–40.Aquatic thrusters are an emerging tool for waterfront landowners to control at a small scale of aquatic plants, algae, and ice cover. Their potential effects to shallow lake aquatic ecosystems remain understudied. During the summer of 2021, 3 aquatic thrusters were installed on docks within the Kawartha Lakes, Ontario, Canada, in a before-after control-impact approach. The aquatic thrusters were operational for 40 d in July to August, 12 h each night, in the shallow nearshore areas, at a fixed horizontal position. Aquatic thrusters significantly reduced plant surface area by 64.9%, floating cover by 70.1%, and plant biomass by 52.0–94.6% in front of the aquatic thrusters, extending approximately 10 m2 into dense aquatic plant beds. Aquatic thrusters caused no significant changes to field water quality, which might have been a result of horizontal positioning of the aquatic thrusters that minimized turbulent water from impacting lake substrates. Further research is required given the potential for aquatic thrusters to affect the biological, physical, and chemical aspects of aquatic ecosystems.

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 score0.137
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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
Published2025
Admission routes2
Has abstractyes

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