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Record W2165565788 · doi:10.1139/s05-026

Water quality and construction materials in rainwater catchments across Alaska

2006· article· en· W2165565788 on OpenAlexvenueno aff
Corianne Hart, Daniel J. White

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsnot available
Fundersnot available
KeywordsRainwater harvestingEnvironmental scienceWater qualityDrainage basinHydrology (agriculture)Tap waterWater pollutionPollutionZincContaminationEnvironmental engineeringEnvironmental chemistryEcologyGeographyChemistryGeology

Abstract

fetched live from OpenAlex

Many residents of Northern regions do not have access to municipal utilities. As such, rainwater catchments are commonly used as an untreated source of drinking water. The water quality in rain catchments depends on the materials used to construct the catchment, the frequency of rainfall, the amount of water collected, the quality of the rainfall, human and animal activity in the surroundings, and the duration of the water storage. The most problematic contaminants in rainwater catchments are bacteria, volatile organic compounds (VOC), and metals such as lead, copper, and zinc. The goal of this project was to help identify the frequency and magnitude of metal contaminants in Alaskan rainwater catchments. Over 50 participants from across Alaska had their tap water sampled for lead, copper, and zinc by atomic absorption during the summer of 2003. Statistical analyses showed strong correlations between catchment material and metal concentrations in drinking water samples. Key words: rainwater catchment, water quality, atomic absorption, lead, copper, zinc.

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 score0.391
Threshold uncertainty score0.177

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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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

Citations30
Published2006
Admission routes1
Has abstractyes

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