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Record W3156704517 · doi:10.1051/e3sconf/202125103001

Food Waste in Developed Countries and Cold Chain Logistics

2021· article· en· W3156704517 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueE3S Web of Conferences · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsTransport Canada
Fundersnot available
KeywordsCold chainBusinessSupply chainProduction (economics)Food wasteAgricultureFood packagingFood processingSupply chain managementFresh foodCommerceAgricultural economicsMarketingWaste managementEngineeringEconomicsFood scienceShelf lifeGeography

Abstract

fetched live from OpenAlex

Food waste is a tough and profound question in the world. Although the development of agricultural technology has effectively promoted the increase of vegetable and fruit production, one-third of global vegetable and fruit production are still wasted. This issue is caused not only by food overproduction or overstock, but also by customers’ requirements for fresh products. This paper aims to thoroughly explore the reasons for food waste and provide some solutions to solve this problem, especially from the “Agri-fresh produce supply chain management” perspective. Solutions include improving the cold-chain logistics system and intelligent methods. To clearly analyze reasons for food wastey, this paper interprets the issue from three dimensions (customer, food supply chain, and farm) and then explores solutions.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.235
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it