Prototype of Fish Drying Device for the Production of Salted Fish Based on IoT
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".