Summary of the 10th International workshop on Models@run.time
Bibliographic record
Abstract
The 10th anniversary of the workshop Models@run.time was held at the 18th International Conference on Model Driven Engineering Languages and Systems. The workshop took place in the city of Ottawa, Canada, on the 29th of September 2015. The workshop was organized by Sebastian Gtz, Nelly Bencomo, Gordon Blair and Hui Song. Here, we present a summary of the discussions at the workshop and a synopsis of the topics discussed and highlighted during the workshop. The workshop received the award for the best workshop at the MODELS 2015 conference out of 18 workshops in total. The award was based upon the organization, program, web site timing and the feedback provided by the workshop participants.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.103 | 0.060 |
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".