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Record W6886042663 · doi:10.14288/1.0373195

Community - Based Water Monitoring : Build Your Own Water Monitoring Logger

2018· article· en· W6886042663 on OpenAlexaboutno aff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsData loggerGlobal Positioning SystemLoggingData collectionAssisted GPSCitizen scienceProxy (statistics)Indigenous

Abstract

fetched live from OpenAlex

Water security for Indigenous communities is an urgent issue. Approximately one third of Indigenous communities in Canada are currently grappling with safe drinking water access, compromised environmental water quality, and associated health issues. Surface water monitoring is often limited in rural and remote regions. This is in part due to the difficulty of accessing remote sites. Even when monitoring is conducted it is usually expensive and intermittent , with results that can be difficult for communities to interpret. There is a need for low-cost, appropriate technology that enables communities to conduct fresh water monitoring. Our Innovation This manual describes how to build a cheap, portable, robust surface water monitoring device. The device is called a “data logger.” It uses open-source technology: anyone can use this Manual to build the device. The logger has a simple design, and can be easily built and repaired with mail-order parts, hence well adapted for use by rural and remote communities. The device can be easily transported by users who are traveling on land or water, or installed at a stationary point. The approximate assembly time is 3–5 hours. The approximate cost is $600, substantially cheaper than commercially available data loggers. What does the data logger test? The logger records electrical conductivity (which is a proxy indicator for contamination) and GPS location. This allows users to collect water data at a precise location, and later return for additional water testing if necessary. This device is not intended as a substitute for lab tests or more sophisticated water monitoring. Rather, the device is designed to enable users to identify specific areas that require more detailed testing. Authors The idea for this logger originated at a Water Bush Camp organized by Caleb Behn (then-Executive Director of Keepers of the Water), in the traditional territory of Halfway River, Saulteau, and West Moberly First Nations at Carbon Lake in 2015. The logger is an adapation of a device built by Dr. Mark Johnson (UBC) to log and monitor water in remote and humid environments in the tropics. While the data loggers previously developed by Dr. Johnson’s group were stationary, the vast reaches of the north inspired a mobile unit that might be towed by a boat and used to sweep for potential contamination. Teddy Eyster, an MSc student working with Dr. Johnson, worked to adapt the monitor for mobile use and test the device in two locations in NWT and Manitoba. This manual was then written by a collective of authors associated with the Sustainable Water Governance and Indigenous Law Project, housed at UBC on the traditional, ancestral, and unceded territory of the Musqueam people.

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 score0.209
Threshold uncertainty score0.977

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.030
GPT teacher head0.215
Teacher spread0.185 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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