{"id":"W1580701341","doi":"10.1007/978-3-540-68880-8_7","title":"A Meeting Scheduling Problem Respecting Time and Space","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Schedule; Computer science; Scheduling (production processes); Interval (graph theory); Subject (documents); Duration (music); Running time; Operations research; Job shop scheduling; Mathematical optimization; Theoretical computer science; Mathematics; Combinatorics; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001320749,0.001398763,0.001892566,0.0009322957,0.001263524,0.002668633,0.00238444,0.001989331,0.008128752],"category_scores_gemma":[0.004720044,0.0007503923,0.001741743,0.002683728,0.0009247749,0.003514548,0.001782579,0.002512173,0.001246142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00130647,"about_ca_system_score_gemma":0.001537293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001832942,"about_ca_topic_score_gemma":0.00148141,"domain_scores_codex":[0.9987836,0.0003093805,0.00009132232,0.000289624,0.0003490974,0.0001769349],"domain_scores_gemma":[0.9978501,0.001210898,0.0002283918,0.0002065027,0.0001941633,0.0003100053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00107599,0.0004160218,0.000672715,0.001129343,0.0002037981,0.0005239362,0.000486792,0.4682398,0.02981408,0.2661378,0.01889598,0.2124037],"study_design_scores_gemma":[0.0002763424,0.0006213855,0.0005559829,0.00007104679,0.0001261802,0.0006655393,0.0003385888,0.7382255,0.006179921,0.227241,0.0256336,0.00006480808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06081985,0.0009683748,0.9044856,0.001401864,0.0003925535,0.0003059942,0.00090823,0.0008091742,0.02990844],"genre_scores_gemma":[0.3679051,0.001090178,0.6063788,0.000265737,0.0005888882,0.0002959816,0.001223842,0.000504389,0.02174707],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008128752,"threshold_uncertainty_score":0.02719343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01287504531396153,"score_gpt":0.2263461921166429,"score_spread":0.2134711468026813,"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."}}