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Record W2100947933 · doi:10.1016/j.jglr.2014.10.002

Scenario analysis: An integrative and effective method for bridging disciplines and achieving a thriving Great Lakes-St. Lawrence River basin

2014· article· en· W2100947933 on OpenAlexafffundvenue
Katrina L. Laurent, Kathryn Bryk Friedman, Gail Krantzberg, Donald Scavia, Irena F. Creed

Bibliographic record

VenueJournal of Great Lakes Research · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicWater Resources and Governance
Canadian institutionsMcMaster UniversityWestern University
FundersNew York Sea Grant, State University of New YorkNatural Sciences and Engineering Research Council of Canada
KeywordsFutures contractThrivingStructural basinScholarshipDrainage basinBridging (networking)Environmental resource managementEnvironmental scienceGeographySociologyPolitical scienceComputer scienceGeologySocial scienceBusiness

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.016
metaresearch head score (Gemma)0.042
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.993
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.042
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0120.009
Science and technology studies0.0040.002
Scholarly communication0.0080.008
Open science0.0030.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0290.002

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 head0.043
GPT teacher head0.401
Teacher spread0.358 · 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
GenreMethods

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

Citations20
Published2014
Admission routes3
Has abstractno

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