Application of RFID Technologies in the Temperature Mapping of the Pineapple Supply Chain
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
Current temperature tracking systems lack the convenience and accuracy demanded by the real conditions of a fast-paced produce supply chain. In recent years RFID technology has been suggested to be an enhanced method for temperature tracking because of its many benefits, such as using little instrumentation, offering the quick readings necessary for real-time decision making, and allowing the capture of long-duration temperature profiles. However, its limitation lies on its failure to provide accurate temperature readings in the critical points of the pallet and the load. The objective of this work was to study the use of RFID in temperature monitoring by comparing the performance of RFID temperature tags versus conventional temperature tracking methods, as well as RFID temperature tags with probe versus RFID temperature tags without probes. Therefore, the temperature mapping of a shipping trial comprising pallets of crownless pineapples instrumented using different RFID temperature dataloggers and traditional temperature dataloggers and packed in two kinds of packages (corrugated boxes and RPC) inside a container was performed. The results showed the many advantages of RFID temperature tracking, such as quick instrumentation and data recovery, and the possibility of accessing the sensor program and data at any point of the supply chain without a line of sight. In addition, the use of RFID tags with probe was justified by its role in determining the efficiency of the precooling operations; while the RFID tags without probe proved useful during transportation and refrigerated storage. The creation of a RFID sensor with a probe, able to record both environmental and probed temperatures is suggested.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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