Bibliographic record
Abstract
Internet of Things (IoT) has evolved in the recent days, connecting almost everything, starting from electronic devices to people, products, and machines together. Data collected from these connections serve as the basis for solving any real-world problems. In a supply chain network, several data are collected across different players and at different levels. The collected data are processed using different techniques to transform data into meaningful information. This gathered information is internal to the organization, helps in improving the internal or immediate supply chain. The information collected can be stored centralized where all the supply chain partners can access and share required information, leading to performance improvement of the entire [1] supply chain. A research framework is proposed which aims to achieve performance improvement along the entire supply chain. The framework starts by explaining how data collection can be done smartly using IoT. Next, how big data analytical tools can be used to transform data into information is discussed. Then, a mathematical model is proposed to measure the performance of internal and immediate supply chain. Then, how information sharing across the different supply chain partners can be achieved using state-of-the-art technologies is explained. Mathematical model proposed is expanded to measure the performance of the external and the entire supply chain. A case study was done to prove the proposed framework. Lastly how this research paves way for future research is discussed.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".