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Record W2528683647

Water, Water Everywhere, But Just How Much is Clean?: Examining Water Quality Restoration Efforts Under the United States Clean Water Act and the United States-Canada Great Lakes Water Quality Agreement

2015· article· en· W2528683647 on OpenAlexaboutno aff
Jill T Hauserman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Resources Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEndangered speciesWater qualityLegislationClean Water ActWildlifeResource (disambiguation)Natural resource economicsWater tradingWater resourcesAgricultureEnvironmental protectionBusinessEnvironmental resource managementEnvironmental planningGeographyEcologyWater conservationEnvironmental sciencePolitical scienceLawBiologyHabitatEconomics
DOInot available

Abstract

fetched live from OpenAlex

If asked to describe what an endangered species is, the average American could likely give a rough definition. Perhaps the World Wildlife Federation and its iconic panda logo comes to mind, or perhaps a favorite endangered species studied in elementary school. But what about an 'endangered' river or lake? A definition or an example of an at-risk water body may be more difficult for the average American to describe. While not 'endangered' under the same definition as an endangered species, water bodies across the North American continent have been designated as 'impaired' or an 'Area of Concern' under United States and Canadian legislation. Regulation of water is of the utmost importance due to the great demands on this resource. A classic example of the importance of water comes from its role in living things. Water comprises up to 60% of the human body, and some organisms derive up to 90% of their body weight from water. Water bodies are an important resource for human survival because they provide drinking water and support species that humans consume, including aquatic species, land animals, and crops. Water is also crucial to support economic activity, as it is a vital component of industry, energy, agriculture, and transportation. The water used in each of these important functions must meet certain quality standards in order to adequately and safely support human survival.

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)0.009
metaresearch head score (Gemma)0.018
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 score0.082
Threshold uncertainty score0.592

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0100.008
Scholarly communication0.0100.007
Open science0.0030.005
Research integrity0.0020.004
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 head0.117
GPT teacher head0.274
Teacher spread0.156 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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Same topicWater Quality and Resources StudiesFrench-language works237,207