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Record W4403905993 · doi:10.59934/jaiea.v4i1.648

Design and Build Temperature and Humidity Control Equipment in IoT-Based Rice Storage

2024· article· en· W4403905993 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

VenueJournal of Artificial Intelligence and Engineering Applications (JAIEA) · 2024
Typearticle
Languageen
FieldEngineering
TopicWireless Sensor Networks and IoT
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsHumidityInternet of ThingsEnvironmental scienceControl (management)Computer scienceTemperature controlAutomotive engineeringAgricultural engineeringEmbedded systemEngineeringControl engineeringMeteorologyGeographyArtificial intelligence

Abstract

fetched live from OpenAlex

This research aims to design and build a temperature and humidity control device in an Internet of Things (IoT)-based rice storage bin using the Blynk platform, ESP8266 module, DHT22 sensor, buzzer, 2-channel relay, fan, and lamp. The quality of rice is greatly affected by the conditions of the storage environment, especially temperature and humidity. Therefore, a system is needed that is able to monitor and control these two parameters in real-time to maintain the quality of rice during storage. The system is designed to use a DHT22 sensor to detect temperature and humidity, and then the data is sent to a ESP8266 microcontroller. A 2-channel relay sets the fan and lights to adjust the temperature and humidity, while the buzzer serves as a warning alarm. In addition, the system is equipped with an automatic notification feature through the Blynk application that informs users if the temperature or humidity exceeds a predetermined limit.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.771
Threshold uncertainty score0.555

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)0.0000.000

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 head0.017
GPT teacher head0.236
Teacher spread0.220 · 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