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Record W3204088534 · doi:10.1111/itor.13061

Ordering and pricing decisions for perishable goods retailer with zero‐inventory and capital constraints

2021· article· en· W3204088534 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

VenueInternational Transactions in Operational Research · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsUniversity of Manitoba
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsProfit (economics)FinanceInternal financingBusinessHeuristicsTrade creditCapital (architecture)Cost of capitalIndustrial organizationMicroeconomicsEconomicsComputer scienceInformation asymmetry

Abstract

fetched live from OpenAlex

Abstract Considering the restriction of optimal profit realizing due to capital constraints in the perishable supply chain, this paper discusses two financing models, crowdfunding financing and bank credit financing from the small–medium enterprise (SME) bank, for the dynamic ordering and pricing system of perishable goods in zero‐inventory under capital constraints. Heuristics algorithms are designed to solve the model. In dynamic crowdfunding financing, retailers bear different demand for capital in different periods and the capital gap for the retailer will narrow over time. In the SME financing model, the requirement for capital depends on retail size. We characterize the conditions of the financing preference between crowdfunding and SME financing channel. Smaller retailers, relative to the bigger players, are more willing to assure higher financing returns to gain the investment.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.074
GPT teacher head0.339
Teacher spread0.265 · 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