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Record W1580386586

Assessment of the capacity for watershed cumulative effects assessment and management in the South Saskatchewan Watershed, Canada

2013· article· en· W1580386586 on OpenAlexaboutno aff
Prakash Basnet

Bibliographic record

VenueUniversity Library - University of Saskatchewan (University of Saskatchewan) · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsWatershedEnvironmental scienceWatershed managementGeographyWater resource managementHydrology (agriculture)GeologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

The cumulative effects of watershed development and large water withdrawals are placing the sustainability of freshwater resources at risk due to alteration of watershed hydrology, stream geomorphology, groundwater recharge, and adverse effects to the aquatic ecology of water resources. The consideration of cumulative environmental effects in development decisions under current project-specific assessment does not fully encompass the interacting effects of multiple stressors over space and time. As a result, the cumulative effects of land uses and development on watershed processes are not properly assessed and managed. There is a recognized need to shift from local, project-scale cumulative effects assessments to broader, landscape, or regional scale assessments to accurately assess cumulative effects to watershed processes and river system condition. The problem is that there is little understanding of the current capacity to do so. This research: i) developed a set of indicators for evaluation of regional capacity to support watershed cumulative effects assessment and management (CEAM) requisites, ii) applied those indicators to the South Saskatchewan Watershed (SSW), iii) identified capacity needs and constraints to watershed CEAM in SSW, and iv) identified lessons learned and opportunities for capacity building to support watershed CEAM principles and practice. Capacity indicator questions were developed for a set of eight institutional requirements for watershed CEAM, identified from a previous study of watershed CEAM in the SSW. Research methods included a web-based survey of academics, regulators, industry and environmental organizations, which consisted of both closed ended and open-ended questions based on the capacity indicators. Survey results were analyzed using the Statistical Package for the Social Sciences and qualitative methods. Results indicate that the primary threats to water quality and quantity in the SSW, as identified by study participants, are broad-scale stressors that are not subject to project-specific environmental assessment regulations. To address these broad-scale stresses, cumulative effects assessment at the regional level needs to be done; however, it was identified that there is currently a lack of mechanisms to support watershed CEAM. The need for a lead agency, multi-stakeholder collaboration, and financial and human resources were identified as the most important requisites from the research results for implementing and sustaining watershed CEAM programs. Research results revealed that watershed CEAM cannot be driven solely ‘bottom-up’ and government must lead watershed CEAM activities. Participants noted that there is collaboration ongoing in the SSW to meet CEAM objectives, but it is limited. There is a lack of clarity around common goals for watershed and sub-watershed management, and a lack of transparency in sharing data. Many participants commented that expertise is available for watershed CEAM, but there is a lack of organizational and financial resources to develop successful plans and actions.

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)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.462

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0030.001
Scholarly communication0.0040.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.182
Teacher spread0.174 · 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

Citations0
Published2013
Admission routes1
Has abstractno

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