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Record W4226200856 · doi:10.22215/etd/2022-14877

The impact of local macrophyte control on lake ecosystems

2022· dissertation· en· W4226200856 on OpenAlexafffundabout
Patrick Beaupré

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsCarleton University
FundersParks Canada
KeywordsMacrophyteBenthic zoneZooplanktonEnvironmental scienceEcosystemMyriophyllumNutrientEcologyAquatic plantGeographyBiology

Abstract

fetched live from OpenAlex

Macrophytes impact the structure and function of freshwater ecosystems but can also have an adverse impact on human activity.While the efficacy of macrophyte control methods are well-known, there are few studies on the environmental impacts of these treatments.In a pair of studies, the impacts of both mechanical cutting and the application of benthic barriers used in the control of the invasive Myriophyllum spicatum on the physical, chemical and biological components of four Ontario Lakes were investigated over four months.A Before-After-Control-Impact study design was employed, and three of the lakes were selected across a nutrient gradient.Observed impacts were minimal and not consistent across lakes.There were no significant differences in control-impact nutrient levels in any of the lakes.Nevertheless, some significant changes were observed for temperature, pH and zooplankton density.Changes to benthic macroinvertebrate and zooplankton community structure were observed in all four lakes.iii

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 score0.075
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.004
GPT teacher head0.240
Teacher spread0.236 · 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 routes3
Has abstractyes

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