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Record W2086911225 · doi:10.1080/20018091095140

Strategies for Protecting and Restoring Rhode Island's Watersheds on Multiple Scales

2001· article· en· W2086911225 on OpenAlexfundno aff
Suzanne M. Lussier, Henry A. Walker, Gerald G. Pesch, Walter Galloway, Robert W. Adler, Michael Charpentier, Randy L. Comeleo, Jane L. Copeland

Bibliographic record

VenueHuman and Ecological Risk Assessment An International Journal · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Resources Studies
Canadian institutionsnot available
FundersWatershed Watch Salmon SocietyU.S. Environmental Protection Agency
KeywordsResource (disambiguation)Environmental resource managementNatural resourceEnvironmental planningGeographic information systemProcess (computing)Water qualityClean Water ActAquatic ecosystemGeographyScale (ratio)Environmental protectionEnvironmental scienceBusinessComputer scienceEcologyCartography

Abstract

fetched live from OpenAlex

The Clean Water Act has traditionally preserved the quality and quantity of a region's water by focusing resources on areas with known or anticipated problems. USEPA Region 1 is taking the supplemental, longer-range approach of protecting areas of New England where natural resources are still healthy. As part of Region 1 's “New England Resource Protection” approach, stakeholders participate in an open process that identifies healthy ecosystems and characterizes how well they support aquatic life and human health. Since the concerns of stakeholders are usually local, the process also displays areas of nonattainment within individual watersheds and determines their likely causes. One of the most powerful ways to display these types of information on multiple scales is to use a geographic information system (GIS). The case of phosphorus in southern Rhode Island's Tucker Pond illustrates how a GIS can help integrate concerns from the public, data from Clean Water Act monitoring, and information from the New England Resource Protection Project to identify types of environmental assessment questions on scales ranging from states to subwatersheds. By involving the public at all stages of the process and better informing them about their watersheds, this new approach makes them better stewards of their environment.

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 score0.020
Threshold uncertainty score0.743

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 studies0.0010.000
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 head0.102
GPT teacher head0.348
Teacher spread0.246 · 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

Citations1
Published2001
Admission routes1
Has abstractyes

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