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Record W2548508951 · doi:10.1111/cag.12323

From boundary waters to watersheds: Legal change and the geography of the Great Lakes‐St. Lawrence system

2016· article· en· W2548508951 on OpenAlexaffvenue
Jamie Benidickson

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicWater Resources and Governance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBoundary (topology)TreatyGeographyDrainage basinStructural basinEcosystem approachEcosystemHydrology (agriculture)Physical geographyGeologyEcologyPolitical scienceGeomorphologyLawCartography

Abstract

fetched live from OpenAlex

Key Messages The legal framework applicable to the geography of the Great Lakes‐St. Lawrence has evolved. At the time of theBoundary Waters Treatya linear, boundary‐oriented perspective prevailed. Following theGreat Lakes Water Quality Agreementof 1972, the introduction of basin‐ and ecosystem‐oriented approaches altered the legal framework for the Great Lakes‐St. Lawrence system.

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)0.003
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.478
Threshold uncertainty score0.962

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.022
Scholarly communication0.0100.006
Open science0.0020.005
Research integrity0.0030.004
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 head0.010
GPT teacher head0.187
Teacher spread0.177 · 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

Citations3
Published2016
Admission routes2
Has abstractyes

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