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Record W4294289312 · doi:10.24018/ejece.2022.6.4.447

Solar Powered Water Pumping System Automation and Control Using a Microcontroller for Aquaculture

2022· article· en· W4294289312 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

VenueEuropean Journal of Electrical Engineering and Computer Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMicrocontrollerWater pumpingAutomationArduinoPIC microcontrollerFish <Actinopterygii>AquacultureWork (physics)Automotive engineeringEnvironmental scienceComputer scienceProcess engineeringEngineeringEmbedded systemFisheryMechanical engineering

Abstract

fetched live from OpenAlex

This work represents an automated solar-powered water pumping system for a fish farm located off-grid in a rural area of Pakistan. The ultrasonic water level sensor is used with the microcontroller Arduino UNO to automate the pumping system. The water level sensor is connected to the microcontroller based on the sensor programming and sensor data extraction from the water ponds of the fish farm. The microcontroller controls the water pump automatically to switch ON/OFF. This solution is very cost-effective for local farmers, and no human is needed on the site to monitor and operate the water pumping system for the fish farm. The designed system can reduce labor costs and effectively provide fish farms a smooth automated water pumping system.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.009
GPT teacher head0.192
Teacher spread0.183 · 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