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Record W4318189255 · doi:10.1109/tits.2022.3230579

Guest Editorial Introduction to the Special Issue on Intelligent Supply Chain in Modern Challenges

2023· editorial· en· W4318189255 on OpenAlexaff
Said M. Easa, Seyed Mohammadreza Ghadiri

Bibliographic record

VenueIEEE Transactions on Intelligent Transportation Systems · 2023
Typeeditorial
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSupply chainSupply chain managementBusinessSupply chain risk managementService managementDemand chainIndustrial organizationPandemicSupply and demandCoronavirus disease 2019 (COVID-19)Risk analysis (engineering)EconomicsMarketingMedicine

Abstract

fetched live from OpenAlex

Ever-Growing supply chain challenges and disruptions directly or indirectly impact all industries. The global pandemic and war situations have caused a significant imbalance in the demand and supply of goods and services worldwide. These horrendous situations elevated the need for more effective supply chain management strategies. Intelligent systems and tech-led solutions are noticeably transforming almost every industry worldwide, and the supply chain and logistics sectors are facing the most significant impact. Many companies worldwide are investing heavily in intelligent supply chain management solutions to address modern global challenges.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.112
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.006

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 head0.043
GPT teacher head0.282
Teacher spread0.239 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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
Published2023
Admission routes1
Has abstractyes

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