Characteristics of The Mackerel Tuna Bone Flour (Euthynnus affinis) Produced by Pressure Hydrolysis Method
Bibliographic record
Abstract
Fish bones are a by-product or waste from fish processing, both on a small and large scale. Many efforts have been made to utilize these bones by converting them into bone meal. The use of the pressure hydrolysis method in the production of fish bone meal can produce high-quality products. This study aimed to determine the quality of the mackerel tuna bone meal using the pressure hydrolysis method. The data obtained were then discussed descriptively and qualitatively. The results showed that the pressure hydrolysis method effectively produced mackerel tuna bone meal. The highest yield of mackerel tuna bone meal was obtained in the TT2 treatment with a heating time of 2 hours. The bone meal produced had moisture content ranging from 6.58% to 8.76%, ash content ranging from 96.86% to 98.82%, and organoleptic values such as odor, texture, and color were acceptable to the panelists. During storage for 3 days at room temperature, there was mold growth of Aspergillus flavus and Aspergillus penicillium in the range of 1.25×102 to 1.45×102 colonies per gram and met the minimum standards set by the Indonesian National Standard (SNI).
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".