Image Processing for Freshness Identification Tilapia Using Backpropagation Algorithm (Case Study: Binjai City Food Security and Agriculture Office)
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.
Bibliographic record
Abstract
Tilapia (Oreochromis niloticus) is a type of fish that comes from rivers and lakes that connect the river. Tilapia was imported to Indonesia officially by the Freshwater Fisheries Research Institute in 1969, Tilapia fish breeds in Indonesia come from Taiwan as for the dark color with vertical stripes as many as 6-8 pieces and the Philippines which is red. The problems faced today related to testing the level of freshness still use conventional methods, namely by only seeing and sorting fish by sight or sight only. This can certainly cause errors in choosing fish for ordinary people or who do not have expertise in choosing fresh fish. For this reason, a system is needed that can identify the freshness of tilapia using digital image management. Many methods are used in identifying an image, one of which is used by using the Backpropagation method.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it