{"id":"W2139177468","doi":"10.3138/infor.49.1.015","title":"A Constraint Optimization Approach for the Allocation of Multiple Search Units in Search and Rescue Operations","year":2011,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Mitacs","keywords":"Guided Local Search; Incremental heuristic search; Search and rescue; Beam search; Best-first search; Computer science; Mathematical optimization; Search algorithm; Search theory; Constraint (computer-aided design); Iterative deepening depth-first search; Constraint programming; Object (grammar); Operations research; Algorithm; Artificial intelligence; Mathematics; Stochastic programming","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.002072985,0.001211428,0.001258667,0.0008884316,0.001011708,0.001746245,0.001982507,0.001807052,0.004257909],"category_scores_gemma":[0.005265181,0.0007413448,0.0009863733,0.002726022,0.001141423,0.00159627,0.001021183,0.001860384,0.0004607697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002007448,"about_ca_system_score_gemma":0.003265701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0261946,"about_ca_topic_score_gemma":0.0205795,"domain_scores_codex":[0.9984438,0.0008264441,0.00006645191,0.0001795432,0.0003582988,0.0001255588],"domain_scores_gemma":[0.9972408,0.002191875,0.00013727,0.00009146988,0.0002548477,0.00008362492],"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":[0.0000437778,0.00004160208,0.0001201271,0.00007195067,0.00003006518,0.00004613194,0.00003814477,0.9587329,0.0004163888,0.01752043,0.000994043,0.02194435],"study_design_scores_gemma":[0.00002039745,0.00002590433,0.00004281076,0.000008361657,0.000008138025,0.00001647743,0.00001451922,0.993764,0.0002368291,0.004727444,0.001127762,0.000007342815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004819121,0.0002929484,0.9913038,0.0002405907,0.00003184913,0.0001121551,0.0001017664,0.00009335074,0.0030044],"genre_scores_gemma":[0.1360752,0.0007396722,0.858348,0.0001653425,0.00005967938,0.0004926412,0.0002550066,0.0001054974,0.003759143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0261946,"threshold_uncertainty_score":0.05208427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1259053238034037,"score_gpt":0.3202113026547849,"score_spread":0.1943059788513812,"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."}}