Bibliographic record
Abstract
Supply chain collaboration is set to accelerate in future years. Evidence from a survey conducted with funding from the Canadian Purchasing Research Foundation is presented and it is argued that understanding of the exploitation of this environment is in its infancy. Recently Athabasca University commenced a research project on supply chain collaboration. Funded and supported by the Canadian Foundation for Innovation, the Alberta Provincial government, SAP and IBM, this project is focused on developing an online model of a fully data integrated supply chain. A simulation model is being used to help us learn how the business community will best use this supply chain environment of the future. Networked private communications between supply team members, data visibility, push versus pull systems, post-simulation performance analysis, group strategy formulation, strategy delivery and team discipline in networked environment are all aspects of research under consideration. A fully functional simulator of a data integrated supply chain environment supported by a complete range of online collaboration tools is currently being field tested and may be demonstrated at this symposium. It is available to researchers online throughout the world to develop their understanding of supply chain collaboration and networked resources management at www.athabascau.ca/scm or www.sccori.com
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".