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Protecting and Managing Water Quality for Health

2011· article· en· W2770297280 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobal Bioethics · 2011
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUnited Nations University Institute for Water, Environment, and Health
Fundersnot available
KeywordsSanitationBusinessSustainabilityPsychological interventionWater supplyWater qualityWastewaterEnvironmental planningClean waterQuality (philosophy)Natural resource economicsEnvironmental resource managementEnvironmental scienceEnvironmental engineeringWaste managementEconomicsEngineeringMedicineEcology

Abstract

fetched live from OpenAlex

While the MDG target for access to improved water supply has been met, questions still exist around the potability and sustainability of those supplies. Moreover, the sanitation target is still unlikely to be met. In order to mitigate water-related health impacts, both now and post-MDGs, it is necessary to coordinate community interventions, focussing not only on water quantity and access, but water quality, sanitation/wastewater treatment and source water protection. While some interventions are technological, we need to invest more money and time in enhancing capacity, providing information and empowering communities to take ownership.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.317

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 head0.162
GPT teacher head0.411
Teacher spread0.250 · 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