Guest Editorial: Fifth Latin American Workshop on Non-Monotonic Reasoning 2009, (LANMR'09)
Bibliographic record
Abstract
This special issue contains a selection of four articles from LANMR’09, the Fifth Latin American Workshop on Non-Monotonic Reasoning 2009. LANMR is an annual event held continuously since 2004. Its main objective is to provide an international forum for discussion and exchange of experiences in formal areas of Computer Science such as Logic, Formal languages, Algorithms, and Non-Monotonic Reasoning. The fifth edition of the workshop, LANMR’09, was placed in the Facultad de Ciencias Basicas, Ingenieŕia y Tecnoloǵia, Universidad Autonoma de Tlaxcala in Apizaco, Tlaxcala, Mexico between 5th and 6th of November of 2009. LANMR’09 received 19 papers, each evaluated by 2 experts in the paper’s main topic. Members of the program committee selected 13 out of the 19 papers for presentation at the workshop. The program committee was conformed by around 23 researchers from around the world, with expertise covering a wide spectrum of formal areas of Computer Science. In addition to the PC members, 6 reviewers collaborated in the evaluation process. The topics addressed at the workshop were wide and rich, such as: Logic programming and nonmonotonic reasoning, Algorithms applied to logic, Answer Set Programming, Knowledge representation, Belief representation, Paraconsistent logics, Deduction techniques, Automated reasoning, Non-classical logics, Reasoning about situations and actions, Planning, Algorithms for graph theory in AI, Multi-agent systems, Preferences, Default and abductive reasoning, and Argumentation. In addition to these presentations, the Workshop had three invited talks and one invited paper. Leopoldo Bertossi, from Carleton University, Ottawa, Canada; Juan Antonio Navarro Perez, from Max Planck Institute for Software Systems, Germany; Cesar Bautista Ramos from Benemerita Universidad Autonoma de Puebla, Mexico; and Luis Moniz Pereira and Alexandre Miguel Pinto, the authors of the invited paper, from Universidade Nova de Lisboa, Caparica, Portugal. As mention above, among the 13 selected papers, the best 4 were selected for publication in this special issue, each evaluated by at least 3 experts in the paper’s main topic. To assure higher standards, a second round of evaluations was performed on these four articles. The selected papers were the following:
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.072 | 0.044 |
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".