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Record W4403905349 · doi:10.59934/jaiea.v4i1.641

Prototype of Fish Drying Device for the Production of Salted Fish Based on IoT

2024· article· en· W4403905349 on OpenAlex
Sri Rezeki, Novriyenni Novriyenni, Milli Alfhi Syari

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
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsFish <Actinopterygii>Production (economics)Internet of ThingsFisheryDried fishBusinessFood scienceEnvironmental scienceComputer scienceChemistryEmbedded systemBiologyEconomics

Abstract

fetched live from OpenAlex

The prototype of the fish drying tool for the production of salted fish is designed to enhance efficiency and control in the salted fish drying process by utilizing IoT technology to monitor and regulate the drying environment conditions. The DHT22 sensor connected to port D5 is used to measure temperature and humidity inside the drying room. The data collected by this sensor is sent to a microcontroller connected to a relay to control the heater and DC fan, as well as a buzzer as a warning system if the room temperature exceeds 60° C. The Blynk application is used for a user interface that allows for the remote monitoring and adjustment of drying parameters via a smartphone. The test results show that this system is capable of maintaining the conditions for drying salted fish within optimal temperature and humidity ranges, thereby improving the quality and efficiency of the drying process. The integration of IoT technology in this device facilitates monitoring and control, as well as enhancing the overall effectiveness of the drying process.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.224

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 head0.055
GPT teacher head0.299
Teacher spread0.244 · 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