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Record W2894997177 · doi:10.1142/s2382624x1850025x

Management of an Aquatic Invasive Weed with Uncertain Benefits and Damage Costs: The Case of <i>Elodea Canadensis</i> in Sweden

2018· article· en· W2894997177 on OpenAlexaboutno aff
George Marbuah, Ing‐Marie Gren, Kristina Tattersdill, Brendan G. McKie

Bibliographic record

VenueWater Economics and Policy · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBiological Control of Invasive Species
Canadian institutionsnot available
FundersVetenskapsrådet
KeywordsWeedElodea canadensisWeed controlBiological dispersalInvasive speciesPopulationAllee effectAbundance (ecology)Environmental scienceEcologyAquatic plantBiologyAgroforestryNatural resource economicsMacrophyteEconomics

Abstract

fetched live from OpenAlex

The invasive aquatic weed Elodea canadensis (Mich) (Canadian pondweed) might provide benefits for nature and society when present in low abundance by contributing to nutrient regulation in lakes, particularly in more degraded environments where native species are unable to persist, but can cause damage when it forms extensive monocultures that choke lake littoral zones. Using a bioeconomic model developed to describe the population dynamics and uncertain spatial dispersion of the weed in Lake Löt in Sweden, we conducted an analysis of optimal management of the species as regards good and bad effects on society. A theoretical finding was that the level of control required depends on the benefits, damage costs, control costs, and uncertainty in dispersal of the weed. Lake Löt was chosen as the case because data on dynamics of the weed are available for this lake. The empirical results showed that the total net benefits were sensitive to inclusion of uncertainty and benefits of the species, but uncertainty had little effect on the level and timing of optimal control of the weed. However, the cost of no action with associated damage costs net of benefits of the weed proved to be considerably larger than the control costs, irrespective of inclusion of benefits and uncertainty.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.946

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 head0.023
GPT teacher head0.223
Teacher spread0.200 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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