{"id":"W4313193649","doi":"10.1609/icaps.v20i1.13398","title":"Preface","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Library science; Operations research; Computer science; Scheduling (production processes); Automated planning and scheduling; Engineering; Artificial intelligence; Operations management","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001268648,0.00007295616,0.00007835607,0.00005715157,0.00009060617,0.0002296161,0.0003478004,0.00003920422,0.00002328632],"category_scores_gemma":[0.0001624905,0.00005859882,0.00002816632,0.0001480791,0.0000289644,0.0002365142,0.0001533348,0.0001200911,0.000002520702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001638913,"about_ca_system_score_gemma":0.00006213276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000241825,"about_ca_topic_score_gemma":3.391615e-7,"domain_scores_codex":[0.9993356,0.000005240864,0.0001508901,0.0001901764,0.0002349621,0.00008306751],"domain_scores_gemma":[0.999381,0.00003083173,0.0001258412,0.00006195056,0.000368978,0.00003136332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002126,0.00005611944,0.03491781,0.00004235341,0.00008670832,0.000004541094,0.00157343,0.007199224,0.09311926,0.8554406,0.0006472524,0.00689151],"study_design_scores_gemma":[0.000179823,0.00001267528,0.01133435,0.0002596676,0.000003935164,0.00004479571,0.0002967461,0.9500672,0.03558847,0.001994916,0.0001320959,0.00008533397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9117507,0.00007865424,0.01745245,0.01378604,0.0008330183,0.0001061211,0.000005253253,0.0006114197,0.05537628],"genre_scores_gemma":[0.9795981,0.00001443565,0.0198611,0.0002063821,0.00001540557,0.000002301381,0.000001284354,0.000003076289,0.0002979297],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.942868,"threshold_uncertainty_score":0.2389591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03375275844167994,"score_gpt":0.2810386256738298,"score_spread":0.2472858672321498,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}