{"id":"W2561815760","doi":"10.1109/ssrr.2016.7784323","title":"Multi-target search strategies","year":2016,"lang":"en","type":"article","venue":"","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Probabilistic logic; Search and rescue; Beam search; Schedule; Context (archaeology); Obstacle; Task (project management); Search algorithm; Machine learning; Artificial intelligence; Search problem; Data mining; Engineering; Algorithm; Robot","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":[0.0001902478,0.0000475022,0.00004547892,0.00005250472,0.00005337214,0.0001638519,0.0004477946,0.00002144331,0.0004589502],"category_scores_gemma":[0.00001286529,0.00002593393,0.00001900212,0.0001455004,0.00003239146,0.0007379375,0.0001395732,0.00003250709,0.0005790766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001157287,"about_ca_system_score_gemma":0.00008777224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001265162,"about_ca_topic_score_gemma":0.000004295634,"domain_scores_codex":[0.9993476,0.0000429714,0.00007794253,0.0001707672,0.0001643402,0.0001963601],"domain_scores_gemma":[0.9995402,0.00004313502,0.000008708571,0.0002510145,0.00007795971,0.00007896268],"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":[0.000003254552,0.0001446259,0.001564495,0.00001139146,0.0000156749,0.00001209466,0.0009105176,0.001996487,0.02108202,0.8951867,0.00541909,0.07365362],"study_design_scores_gemma":[0.001890651,0.0001871931,0.004703979,0.00002901274,8.153726e-7,0.00001009302,0.0002712347,0.9202587,0.02604102,0.01322168,0.03292536,0.0004602428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002499656,0.00001091142,0.9757116,0.003483091,0.00005825457,0.00006742634,5.164316e-7,0.0001990496,0.02021923],"genre_scores_gemma":[0.3111863,0.00002030631,0.6745725,0.0002183368,0.00001369385,0.000005154595,2.818246e-7,0.000003480233,0.01398],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9182622,"threshold_uncertainty_score":0.7443052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04783042107704689,"score_gpt":0.3053430905315728,"score_spread":0.2575126694545259,"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."}}