{"id":"W4312950274","doi":"10.1609/icaps.v21i1.13439","title":"Preface","year":2011,"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":"University of Toronto","funders":"","keywords":"Library science; Computer science; Operations research; Presentation (obstetrics); Engineering; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001502816,0.00007819419,0.00007324387,0.00008484145,0.00008191103,0.0001082322,0.0004972718,0.0000396823,0.00002871489],"category_scores_gemma":[0.00007900767,0.00005916485,0.00002514344,0.0001092145,0.00004008963,0.0002833393,0.0001252188,0.0001121844,0.000003350618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001314357,"about_ca_system_score_gemma":0.00002733628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008860156,"about_ca_topic_score_gemma":2.781783e-7,"domain_scores_codex":[0.999386,0.000003841559,0.0001542205,0.0001693392,0.0002000392,0.00008658625],"domain_scores_gemma":[0.9995301,0.00001722687,0.0001533209,0.00005780381,0.0002086524,0.00003291605],"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":[0.00004341528,0.0000557427,0.05816582,0.00002731993,0.00006932646,0.000001315001,0.004823127,0.0008536528,0.01848483,0.9117535,0.0003070699,0.005414821],"study_design_scores_gemma":[0.0001868381,0.00003396055,0.03365169,0.00023159,0.000004623306,0.00002161854,0.0003284809,0.9428864,0.01889272,0.003616882,0.00004050141,0.000104746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8667898,0.00002528505,0.01593133,0.002200627,0.0006412949,0.0001463885,0.00000321658,0.0006858417,0.1135762],"genre_scores_gemma":[0.9733481,0.000006953478,0.0263892,0.000110827,0.00001037124,0.000003137523,4.309287e-7,0.000003239441,0.0001277754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9420327,"threshold_uncertainty_score":0.2412672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06110902835775434,"score_gpt":0.2727059875151443,"score_spread":0.21159695915739,"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."}}