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Record W3045521130 · doi:10.1111/gwmr.12408

Building a <scp>Low‐Cost</scp> , <scp>Internet‐of‐Things</scp> , <scp>Real‐Time</scp> Groundwater Level Monitoring Network

2020· article· en· W3045521130 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueGroundwater Monitoring & Remediation · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsUploadReal-time computingGroundwaterWater levelThe InternetInternet of ThingsComputer scienceEnvironmental scienceEngineeringEmbedded systemOperating systemGeography

Abstract

fetched live from OpenAlex

Abstract A community‐based, real‐time, groundwater level monitoring network consisting of 11 sites was built in Nova Scotia, Canada, using privately owned domestic wells and low‐cost, custom‐made water level meters. The real‐time meters use an ultrasonic sensor to measure water levels and an Internet‐of‐Things device to transmit the data to the Internet by WiFi or cellular connection. The water level data are plotted in real‐time on a time‐series graph and are available immediately for online viewing and downloading. Based on observations at three sites, the real‐time water level meter data compare well to pressure transducer measurements, with mean absolute errors of less than 0.02 m. The meters are simple to build, and components are readily available from online suppliers at low cost.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0020.002
Research integrity0.0010.001
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 head0.042
GPT teacher head0.257
Teacher spread0.215 · 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