Evolution of Radio Frequency Identification (RFID) in Agricultural Cold Chain Monitoring: A Literature Review
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
Radio Frequency Identification (RFID) is a technology providing considerable opportunities to improve quality control for perishable foods. Over the past decade, a significant improvement in RFID application has been observed in cold chain monitoring. The aim of this paper is to, first, demonstrate the role of RFID in improving the monitoring of the agricultural products cold chain. Particular focus is placed on the specifications of RFID and its advantages, which makes its application appealing in food temperature monitoring. Second, this paper aims to provide an overview of RFID developments in cold chain monitoring. For this purpose, we conduct a review of the literature throughout 2004-2018 citing the challenges of this technology’s practical implementation in temperature monitoring of perishables, and provide the solutions presented in the literature for each limitation. This survey would be beneficial for those involved in food distribution, as it offers approaches for overcoming the limitations of RFID, making its application more advantageous.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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