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Record W324653448 · doi:10.5070/g313724071

A SWOT Analysis of the Great Lakes Water Quality Protocol 2012: The Good, the Bad and the Opportunity

2014· article· en· W324653448 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueElectronic Green Journal · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSWOT analysisScope (computer science)Protocol (science)Strengths and weaknessesQuality (philosophy)Environmental planningBusinessEnvironmental resource managementIndigenousRisk analysis (engineering)Computer scienceEcologyGeographyEnvironmental scienceMarketingMedicinePsychologyBiology

Abstract

fetched live from OpenAlex

Since the signing of the Great Lakes Water Quality Protocol by Canada and the United States on September 7, 2012, there has been no review of it in the literature. This paper aims to fill that gap by conducting a Strength, Weakness, Opportunity and Threats (SWOT) analysis that will aid in deducing strategies to maximize the strengths and opportunities and minimize the weaknesses and threats to achieving the purpose of the Protocol. The review found that the Protocol has maintained the basic visionary infrastructure retaining the purpose and main objectives while broadening the scope to include three new Annexes; Aquatic Invasive Species, Habitat and Species and Climate change. Weaknesses include instances of ambiguous language, the separate treatment of groundwater, lack of Annex on Indigenous engagement and discrepancies between the principles and the Annexes. A key threat remains the lack of resources for the implementation of the Protocol.

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.

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)0.006
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.015
GPT teacher head0.262
Teacher spread0.248 · 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