Bibliographic record
Abstract
Collection of selected, peer reviewed papers from the ICMEP 2013 International Conference on Manufacturing Engineering and Process, April 13-14, 2013, Vancouver, Canada. The 373 papers are grouped as follows: Chapter 1: Advanced Materials Engineering and Technology; Chapter 2: General Mechanical Engineering;Chapter 3: Design Technology and Engineering;Chapter 4: Applied Thermodynamics, Heat Transfer, Energy Conversion; Chapter 5: Electrical Engineering and Electric Machines; Chapter 6: Power System and Energy Engineering: Its Applications; Chapter 7: Instrumentation, Measurement Technologies, Analysis and Methodology; Chapter 8: Electronics and Integrated Circuits, Embedded Technology and Applications; Chapter 9: Mechatronics and Robotics; Chapter 10: Modern Control, Automation and Reverse Engineering; Chapter 11: New Technology, Method and Technique in Civil Engineering; Chapter 12: Manufacturing and Industrial Engineering, Management Applications; Chapter 13: Mathematics - in Particular, Calculus, Differential Equations, Statistics, and Linear Algebra; Chapter 14: Signal Processing and Data Mining;Chapter 15: Information Technologies and Networks: Its Applications.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.024 |
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".