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Record W2291253022 · doi:10.1142/s1464333216500010

Monitoring <i>Nûtimesânân</i> Following the Diversion of Our River: A Community-led Registry in Eeyou Istchee, Northern Québec

2016· article· en· W2291253022 on OpenAlexaffabout
Ronald Edward Strangway, Marc Dunn, Ryan Erless

Bibliographic record

VenueJournal of Environmental Assessment Policy and Management · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsNiskamoon Corporation
Fundersnot available
KeywordsHydroelectricitySubsistence agricultureFish migrationStakeholderEnvironmental planningEnvironmental resource managementStakeholder engagementGeographyFish <Actinopterygii>BusinessFisheryEngineeringEnvironmental scienceEcologyPolitical science

Abstract

fetched live from OpenAlex

The Waskaganish Voluntary Anadromous Cisco Catch Registry is a community-led ex post monitoring programme carried out within the Environmental Impact Assessment (EIA) Follow-up Phase of the Rupert River Diversion Hydroelectric Project. The Registry monitors an aboriginal subsistence fishery in the Cree community of Waskaganish. Due to the complexity of the socio-ecological system, predicting the project’s impacts on the fishery at the ex ante stage proved difficult. The programme has allowed the community to monitor changes in the cisco fishery, while also providing a forum for communication and collaboration with the proponent, Hydro-Québec. The programme recognises and incorporates both local ecological knowledge and scientific results from site-specific biological monitoring studies. Overall, the Registry has enhanced stakeholder understanding of project impacts, improved mitigation management decision-making and led to the development of an effective consultation framework. Most importantly, the Registry has helped the fishery to continue into the future despite project impacts.

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 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.636

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.010
GPT teacher head0.277
Teacher spread0.267 · 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 teacher head, 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

Citations8
Published2016
Admission routes2
Has abstractyes

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