Bibliographic record
Abstract
Reproducible outcomes from the CAV paper "Fast Termination and Workflow Nets" by Piotr Hofman, Filip Mazowiecki and Philip Offtermatt. The artifact contains implementations of several procedures related for fast termination in workflow nets, such as deciding whether a workflow net is terminating fast, and computing more fine-grained constants related to termination. The implementation is written as an extension to FastForward, a tool for decision and optimization procedures on Petri nets. This artifact includes a virtual machine image, which is equipped with the source code of the artifact, a benchmark suite of workflow nets, and instructions on how to reproduce the experiments from our paper. You can open the VM image e.g. with Virtualbox. A reviewer reported issues with unzipping the zip file, obtaining the following error message when running ´unzip´: Archive: FastForwardForTermination.zip?download=1 warning [FastForwardForTermination.zip?download=1]: 5447050078 extra bytes at beginning or within zipfile (attempting to process anyway) error [FastForwardForTermination.zip?download=1]: start of central directory not found; zipfile corrupt. (please check that you have transferred or created the zipfile in the appropriate BINARY mode and that you have compiled UnZip properly) 1 archive had fatal errors. A workaround to this problem is to run ` zip -FF -fz FastForwardForTermination.zip -O FastForwardForTermination.fixed.zip`, then `unzip FastForwardForTermination.fixed.zip`
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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.111 | 0.068 |
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".