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Record W3099775788

A State-Provincial Approach to Harmful Algal Blooms in the Great Lakes Basin: Possibilities and Pitfall

2020· article· en· W3099775788 on OpenAlexaboutno aff
Irene Creed, Kathryn Bryk Friedman

Bibliographic record

VenueeYLS (Yale Law School) · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAlgal bloomState (computer science)Environmental resource managementStructural basinEnvironmental scienceGeographyEnvironmental planningEnvironmental protectionWater resource managementEcologyGeologyComputer sciencePhytoplanktonBiology
DOInot available

Abstract

fetched live from OpenAlex

Event Description and Panels Harmful algal blooms (HABs) are negatively impacting the Great Lakes. The Lakes should be enjoyed for recreation and serve as the home to bountiful freshwater species, however, this precious resource is becoming increasingly toxic. Much has been written and studied about the causes of HABs, and numerous solutions have been proposed. However, there are many cooks in the remedial kitchen and the progress toward remediation has been stalled and uncoordinated. This symposium will present such a framework by two leading scientific and legal experts on HABs. Professor Irena Creed, Associate Vice President for Research at the University of Saskatchewan and Fellow of the Royal Society of Canada, and Kathryn Friedman, Global Fellow at the Woodrow Wilson International Center for Scholars Canada Institute and Research Professor at the University at Buffalo, will propose a state-provincial framework for tackling HABs. This proposal will then be reviewed by a panel of federal, state and provincial environmental regulators and a panel of industry and non-government organization stakeholders. The timely questions to be answered include: Whether a state-provincial framework necessary? Is it possible? What are the challenges? How can an enforceable and successful framework be created? Government Regulatory Panel: Michigan: Teresa Seidel, Division Director for Water Resources, Department of Environment, Great Lakes & Energy, State of Michigan Minnesota: Katrina Kessler, Assistant Commissioner for Water and Agriculture Policy, Minnesota Pollution Control Agency, State of Michigan New York: Karen Stainbrook, Chief of Lake Monitoring and Assessment Section, Department of Environmental Conservation, State of New York Wisconsin: Madeline Magee, PhD, Great Lakes and Mississippi River Monitoring Coordinator, BEACH Program Manager, Office of Great Waters – Great Lakes and Mississippi River, Department of Natural Resources, State of Wisconsin Halton, Ontario: Chitra Gowda, M.Sc. Environmental Engineering, Sr. Manager, Watershed Planning and Source Protection, Conservation Halton International Joint Commission: Dr. Lucinda Johnson, Member of the IJC's Science Advisory Board, Associate Director and Water Initiative Director, University of Minnesota Natural Resources Research Institute, Duluth, MN Environment and Climate Change Canada: Ms. Tricia Mitchell, Acting Associate Regional Director General, Ontario Region, ECCC's Strategic Policy Branch Academic and NGO Panel: Howard Learner, Executive Director, Environmental Law and Policy Center (Chicago, IL) Todd Brennan, Senior Policy Director, Alliance for the Great Lakes, (Green Bay, WI) Noah Hall, Professor of Law, Wayne State University School of Law Diane Dupont, Scientific Director, Water Economics, Policy and Governance Network, Brock University

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)0.007
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.403
Threshold uncertainty score0.810

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0090.005
Scholarly communication0.0080.004
Open science0.0030.005
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0070.000

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 head0.013
GPT teacher head0.210
Teacher spread0.197 · 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 designTheoretical or conceptual
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
Published2020
Admission routes1
Has abstractyes

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