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Record W775847654 · doi:10.18060/20959

Laurentian and African Great Lakes--Different Strategies in the Fight Against Invasive Species

2013· article· en· W775847654 on OpenAlexaff
Scott O. McKenzie

Bibliographic record

VenueIndiana international & comparative law review · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Biodiversity
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInvasive speciesIntroduced speciesLegislationGeographyEcologyBiodiversityEcosystemEnvironmental resource managementEnvironmental planningBiologyPolitical scienceEnvironmental science

Abstract

fetched live from OpenAlex

Invasive exotic species are a reality in all ecosystems. These biological invaders disrupt ecological patterns and cause billions of dollars in economic damage. Justifiably, governments are stepping up their response. However, while many invaders are considered unmitigated ecological disasters, a number of species have become important and controversial parts of the regional economy. In the Laurentian Great Lakes, the invasive species issue has been addressed through a number of unilateral and multilateral attempts at the state, national, and international level. This “law of the lakes” has evolved towards the implementation of the Great Lakes Water Quality Agreement of 2012, which uses a framework-protocol basis to combat the problem through a preservation-focused ecosystem approach. The management of water and fisheries in the African Great Lakes has similar problems addressing invasive species. However, states in this region have responded to the threat differently, particularly as it pertains to economically viable invasive fish species. Various state-level legislation and policy shows that the invasive threat is acknowledged, but follows a conservation management approach, which hopes to maintain the essential economic opportunities that the invasive species provide for area residents. The experiences of the Laurentian Great Lakes in moving their invasive species management forward can be used as a template to update and focus the response in the African Great Lakes.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.997

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)0.0050.001

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 head0.041
GPT teacher head0.260
Teacher spread0.220 · 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.

Study designNot applicable
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
Published2013
Admission routes1
Has abstractyes

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