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Record W2284596777

Contrasting Mid-West United States Wetlands Policy with Western Canadian Watershed Governance

2011· article· en· W2284596777 on OpenAlexaboutno aff
Nicholas P. Guehlstorf, Lars Hällström

Bibliographic record

VenueSSRN Electronic Journal · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Conservation and Management
Canadian institutionsnot available
Fundersnot available
KeywordsWatershedCorporate governanceWatershed managementClean Water ActSubsidyIncentiveWetlandBusinessEnvironmental planningEnvironmental resource managementGeographyPolitical scienceEconomicsFinanceEcologyWater quality
DOInot available

Abstract

fetched live from OpenAlex

Starting in the 1970s, watershed regulations in the United States have been either (1) permits for managing a proposed land used activity, or (2) enforcing standards for controlling a point or non-point source pollutant. In the 1990s, alternatives to the command and control system of wetland policy were generally market-based incentives to encourage a cost-effective means of environmental conservation or subsidized community participation campaigns to preserve the resource. While states and regions have different programs, banking mitigation is the U.S. federal policy for watershed management and wetland preservation. In Canada, socio-political and geographic factors impact the governance and regulation of watersheds more than the United States. For example, watershed laws in Alberta are increasingly similar to the regulatory regimes and risk analysis used in the States. This study is a comparison of watershed management practices and problems in the Mississippi River Valley in the United States to the wetland management ideas and initiatives of Alberta Canada. It is our thesis that the banking mitigation strategies of Illinois and Missouri have demonstrated environmental risks that should be considered by the people and politicians of the newly proposed mitigation initiatives in Alberta.

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)0.000
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 score0.449
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.188
Teacher spread0.180 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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