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Record W2907466780 · doi:10.1109/icsens.2018.8589802

Low-Cost 3D-Printed Wireless Soil Moisture Sensor

2018· article· en· W2907466780 on OpenAlexaff
Muhammad Fahad Farooqui, Ahmed A. Kishk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsConcordia University
Fundersnot available
KeywordsWater contentMoistureEnvironmental scienceAntenna (radio)Remote sensingFabricationWirelessWireless sensor networkSoil moisture sensorAltitude (triangle)Computer scienceElectronic engineeringEngineeringMaterials scienceGeologyTelecommunications

Abstract

fetched live from OpenAlex

A fully passive, 3D-printed wireless sensor for detecting soil moisture levels is presented in this paper. The sensor comprises a capacitively loaded antenna whose resonant frequency changes in response to soil moisture level. Results show that a variation in soil moisture levels of 20% by weight causes a change in frequency of 16.66 %. A low-cost sensing scheme for large area soil monitoring is proposed in which the sensors are dispersed in the agricultural land and work in combination with unmanned aerial vehicles (U A V s) which record the sensor readings and send the data to a ground station. Calculations show that the sensors can be read by a UAV flying at an altitude of 298 m. Additive manufacturing technique namely 3D-printing has been used for the fabrication of the sensor.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.005

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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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; both teacher heads agree on what is shown here.

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

Citations14
Published2018
Admission routes1
Has abstractyes

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