Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This volume contains the Proceedings of the First International Workshop on Bounded Model Checking Methods (BMC′2003). The Workshop was held in Boulder, Colorado, USA on July 13, 2003, as affiliated workshop to CAV′2003. The objective of BMC′03 was to bring together scientists from academia and industry to report and debate advances in Bounded Model Checking and related issues. Since its introduction in 1999 by Biere, Cimatti, Clarke and Zhu, Bounded Model Checking has been adopted by most relevant companies as a complementary technique to the more traditional BDD based unbounded symbolic model checking. Largely due to the advances in SAT technology in the last few years, Bounded Model Checking became a leading tool in detection of relatively shallow logical errors, outperforming BDD based tools in most of these cases. The large interest in this technology has created a constant stream of new ideas and improvements that make this technique more and more useful. The papers in this volume were reviewed by the program committee consisting, besides the two editors, of David Basin ( Freiburg Univ., Germany) Per Bjesse ( Synopsys, USA) Alessandro Cimatti ( IRST, Italy) Raanan Fraer ( Intel, Israel) Danny Geist ( IBM HRL, Israel) Alan Hu ( Univ. of British Columbia, Canada) James Kukula ( Synopsys, USA) Ken McMillan ( Cadence, USA) Sharad Malik ( Princeton Univ., USA) Mary Sheeran ( Chalmers Univ. of Technology, Sweden) Joao M. Silva ( Technical Univ. of Lisbon, Portugal) Toby Walsh ( Univ.of York, UK) Yunshan Zhu ( Synopsys, USA) Extended versions of some of these papers are considered to be published in a forthcoming special issue of the international Journal on Software Tools for Technology Transfer (STTT). July 13, 2003 Ofer Strichman Armin Biere
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it