Bibliographic record
Abstract
This is a Manager's guide to Microsoft - and how to use it to improve supply chain coordination and profitability at every level.Our implementation team - particularly myself - really benefited from finally grasping the overall picture, and the suggested changes that improved system usage - Terry Cook, CFO, Delkor Systems (USA). Bridging the gap between user manuals and real-life implementation, this is a must read for anyone implementing in manufacturing or distribution - Grant Barkman, Consulting Manager, CSB Systems (Canada).An easy-to-follow yet comprehensive guide that provides a 24/7 consultant for those implementing supply chain management with Navision - Michael Anderson, Director, Enterprise Solutions Group (Asia). Fast-track learning was critical for our ERP team, and Dr. Hamilton's clear and succinct explanations reflect a rare skill in presenting complex subjects simply. - Myles Halsband, President, The Abacore Group (USA). A refreshingly straightforward explanation of how to use for managing supply chain activities, especially for executives responsible for operations - Mihai Charita, Managing Director, LLP Group (Europe). A clear and practical explanation that was invaluable in helping us evaluate and ultimately select as the ERP system for running our business - Karen Slatter, President, Bench Dog Tools (USA). Scott Hamilton, Ph.D., has specialized in ERP information systems for manufacturing for more than 30 years. He is the author of Maximizing Your ERP System.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.176 | 0.152 |
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".