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Record W1589963289 · doi:10.1029/2010wr010213

Probing the integration of land use and watershed planning in a shifting governance regime

2011· article· en· W1589963289 on OpenAlexaffabout
Ryan Plummer, Danuta de Grosbois, Rob de Loë, Jonas Velaniškis

Bibliographic record

VenueWater Resources Research · 2011
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversity of WaterlooUniversity of GuelphBrock University
Fundersnot available
KeywordsWatershedWatershed managementCorporate governanceEnvironmental planningIncentiveWater resourcesEnvironmental resource managementBusinessLand useLand-use planningWater resource managementEnvironmental scienceEngineeringCivil engineeringComputer scienceEconomicsEcologyFinance

Abstract

fetched live from OpenAlex

Effective governance that contributes to the integration of water management and land use planning is essential for successful protection of drinking water sources and, ultimately, provision of safe drinking water. In many jurisdictions, land use planning and watershed management occur on separate tracks. This article examines the prospects for integration of these two critical processes. A multicase study approach is used, focusing on the specific objective of protection of drinking water sources. Experiences in three case study watershed regions in Ontario, Canada (Grand River, Upper Thames, and Lake Simcoe), were analyzed. The goal was to identify the extent to which source water protection components and indicators are expressed in land use and watershed‐based planning documents. Similarities and differences among the watershed regions are distinguished through a cross‐case analysis. The results suggest that a shifting governance regime for drinking water safety in Ontario is contributing to integration between land use and water management. However, proactive and ongoing efforts are required to ensure that integration occurs and that barriers to integration are addressed. Timely guidelines, incentive‐based tools, up‐to‐date and accurate information, and adequate financial resources are essential.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.230

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 communication0.0000.000
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 head0.083
GPT teacher head0.267
Teacher spread0.184 · 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

Citations47
Published2011
Admission routes2
Has abstractyes

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