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Record W4235169269 · doi:10.32920/ryerson.14664987.v1

Orange cold chain with multiple nutritional components

2021· preprint· en· W4235169269 on OpenAlexaff
Muhammad Kamran

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCold chainSupply chainGenetic algorithmValuation (finance)MATLABIndex (typography)Computer scienceMathematical optimizationMathematicsBusinessEngineering

Abstract

fetched live from OpenAlex

Two echelon cold supply chain model is developed in which warehouse and retailer are the two main actors of supply chain. The model is based on energy consumption cost for chiller system and the stochastic lead time. To incorporate the quality degradation, global stability index (GSI) method is used. The objective is to analyze the effect of retailer’s storage temperature and nutritional index weightage on the total cost of supply chain. A breakdown structure of all the associated costs is developed to formulate the total cost of cold chain. A numerical example is used for better understanding. To find the optimal solution, the model is numerically solved by using matlab genetic algorithm. The sensitivity analysis is being performed to study the model behavior against different parameters. Keywords: Cold chain, echelon valuation, quality degradation, global stability index, stochastic lead time

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.780

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)0.0010.000

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 head0.029
GPT teacher head0.216
Teacher spread0.186 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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