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Record W2341936096 · doi:10.1093/biosci/biw030

Formal Integration of Science and Management Systems Needed to Achieve Thriving and Prosperous Great Lakes

2016· article· en· W2341936096 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBioScience · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicWater Resources and Governance
Canadian institutionsTrent UniversityWestern University
FundersCanadian Water Network
KeywordsThrivingStandardizationEnvironmental resource managementBusinessRisk analysis (engineering)Risk managementEnvironmental planningEnvironmental scienceComputer scienceFinance

Abstract

fetched live from OpenAlex

For over a century, governments on both sides of the Canada–US border have employed diverse policy instruments and management tools to protect the Great Lakes. This crucial freshwater resource continues to show signs of degradation. We explore how the International Organization for Standardization Risk Management Standard (ISO 31000) can be used by governments to reduce the risk of failing to achieve the policy objectives of the Great Lakes. ISO 31000 facilitates the analysis of human activities that drive the causal pathways of ecosystem pressures–effects–impacts and analyzes the links between these causal pathways and the performance of management measures operating within the Great Lakes. ISO 31000 allows governments to shed light on why, despite best intentions, management measures are not working and enables governments to continually improve the management system until the risks of policy failures are reduced to acceptable levels, bringing new hope to the future of the 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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.258
Teacher spread0.240 · 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