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Record W6991400899

Great Lakes: EPA Needs to Define Organizational Responsibilities Better for Effective Oversight and Cleanup of Contaminated Areas

2002· report· en· W6991400899 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of North Texas Digital Library (University of North Texas) · 2002
Typereport
Languageen
FieldSocial Sciences
TopicWater Resources and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsRemedial actionAgency (philosophy)Quality (philosophy)Water qualityRemedial educationResource (disambiguation)State (computer science)Environmental quality
DOInot available

Abstract

fetched live from OpenAlex

A letter report issued by the General Accounting Office with an abstract that begins "To protect the Great Lakes and to address common water quality problems, the United States and Canada entered into the bilateral Great Lakes Water Quality Agreement in 1972. The agreement has been amended several times, most recently in 1987. That year, the two countries agreed to cooperate with state and provincial governments to develop and implement remedial action plans (RAPs) for designated areas in the Great Lakes Basin--areas contaminated, for example, by toxic substances. The Environmental Protection Agency (EPA) leads the effort to meet the goals of the Great Lakes Water Quality Agreement, which include RAP development and implementation. As of April 2002, all of the 26 contaminated areas in the Great Lakes Basin that the United States is responsible for have completed the first stage of the RAP process; however, only half have completed the second stage. Even though EPA has been charged with leading the effort to meet the goals of the agreement, it has not clearly delineated responsibility for oversight of RAPs within the agency, and, citing resource constraints and the need to tend to other Great Lakes priorities, reduced its staff and the amount of funding allocated to states for the purpose of RAP development and implementation."

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 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 score0.346
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.000
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 head0.012
GPT teacher head0.189
Teacher spread0.177 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

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