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Record W2021373096 · doi:10.1142/s1464333211004012

ADVANCING WATERSHED CUMULATIVE EFFECTS ASSESSMENT AND MANAGEMENT: LESSONS FROM THE SOUTH SASKATCHEWAN RIVER WATERSHED, CANADA

2011· article· en· W2021373096 on OpenAlexaffabout
Bram Noble, Poornima Sheelanere, Robert Patrick

Bibliographic record

VenueJournal of Environmental Assessment Policy and Management · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsUniversity of Saskatchewan
FundersAustralian Government
KeywordsWatershedCumulative effectsContext (archaeology)Environmental planningWatershed managementCLARITYEnvironmental resource managementStressorGeographyEnvironmental scienceEcologyPsychologyComputer science

Abstract

fetched live from OpenAlex

Watersheds are under increasing pressures from the cumulative environmental effects of human actions. Reviews of recent practice suggest that cumulative effects assessment and management (CEAM) has failed to capture the full range of stressors to Canada's watersheds. Indeed, the limitations to CEAM have been well documented; yet, there has been limited constructive evaluation to help explain why CEAM has failed to advance. In this paper we examine the underlying challenges to the assessment and management of cumulative effects in a watershed context. Based on lessons emerging from the South Saskatchewan watershed, Canada, challenges to CEAM in watersheds include stakeholder understanding and interpretation of cumulative effects; limitations in the scale of current EA practices in watersheds; data challenges; the lack of established thresholds for watershed effects; and lack of clarity regarding watershed science and regulatory capacity. The merits of a watershed-based approach to CEAM to help address these challenges to practice are explored.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.012
GPT teacher head0.266
Teacher spread0.254 · 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.

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

Citations28
Published2011
Admission routes2
Has abstractyes

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