Traceability of Sustainability and Safety in Fishery Supply Chain Management Systems Using Radio Frequency Identification Technology
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
At present, sustainability and emerging technology are the main issues in any supply chain management (SCM) sector. At the same time, the ongoing pandemic is increasing consumers' concerns about food safety, processing, and distribution, which should meet sustainability requirements. Thus, supervision and monitoring of product quality with symmetric information traceability are important in fresh food and fishery SCM. Food safety and traceability systems based on blockchain, Internet of Things (IoT), wireless sensor networks (WSN), and radio frequency identification (RFID) provide reliability from production to consumption. This review focuses on RFID-based traceability systems in fisheries' SCM, which have been employed globally to ensure fish quality and security, and summarizes their advantages in real-time applications. The results of this study will help future researchers to improve consumers' trust in fisheries SCM. Thus, this review aims to provide guidelines and solutions for enhancing the reliability of RFID-based traceability in food SCM systems so to ensure the integrity and transparency of product information.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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