Cold chain management of perishable distribution in northern communities of Canada.
Bibliographic record
Abstract
Transport of perishables, such as fruits and vegetables, to isolated northern communities as part of Canada Post, and Indian and Northern Affairs Canada Food Mail Program has always been a big challenge. Because of the unique conditions (extreme cold, strong winds, snow storms, abundant rain) encountered throughout the distribution process, the cold chain is rarely maintained resulting in enormous losses of perishables. The objective of this project was: 1) study the actual situation of the cold chain; 2) identify the weaknesses of the distribution process. In order to learn about the conditions encountered during transportation and to identify the critical points, temperature and relative humidity were monitored with sensors placed inside the loads and inside the cargo holds of the airplane. Qualities of fruits and vegetables, handling procedures and packaging materials were accessed at each transit point. Results showed that several aspects of the distribution process needed to be improved in order to deliver fruits and vegetables with a better quality. Thus, improvement of the cold chain management, handling operations, and development of new packaging are necessary. Furthermore, this complex distribution process needs to be accessed as a whole since one missing step in the cold chain may jeopardize the entire effort.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".