Bibliographic record
Abstract
This special issue of the Brazilian Journal of Operations and Production Management presents some of the best papers of the ICPR Americas 2006. The third edition of the American International Conference on Production Research (ICPR) was held in Curitiba, Brazil, from July 30th thru August 2nd, 2006, and was organized by the Brazilian Association of Production Engineering (ABEPRO), and the Industrial & Systems Engineering Graduate Program (PPGEPS) of the Pontifi cal Catholic University of Paraná (PUCPR). It was an offi cial Conference of the International Foundation for Production Research (IFPR).There were about 200 papers submitted from Brazil, Argentina, Canada, Chile, Colombia, Costa Rica, England, France, Germany, Japan, Mexico, Portugal, Russia, Turkey and United States. A double-blind review process selected approximately 100 papers for oral presentations (allocated in 5 Technical sessions) and 30 papers as posters. Among them, best ones were selected and the authors were invited to submit a more robust article. Then, these papers were double-blind reviewed and some of those that achieved the higher marks were selected to be part of this issue.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.169 | 0.078 |
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".