{"id":"W2162626399","doi":"10.1109/sysose.2008.4724181","title":"Enhancing canine disaster search","year":2008,"lang":"en","type":"article","venue":"","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Toronto Metropolitan University","funders":"","keywords":"Urban search and rescue; Search and rescue; Computer science; Disaster area; Robot; Human–computer interaction; Artificial intelligence; Mobile robot; Geography","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":[0.0002545471,0.000381325,0.0003973445,0.0003735934,0.0002718553,0.0004773065,0.0004711272,0.0004657251,0.003518073],"category_scores_gemma":[0.0008974412,0.0001596371,0.0002128326,0.0003119013,0.0002510965,0.0009690213,0.001007227,0.0002973095,0.0009308068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001651786,"about_ca_system_score_gemma":0.0002787447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008490026,"about_ca_topic_score_gemma":0.001805316,"domain_scores_codex":[0.9998052,0.00003784665,0.000006491647,0.00003922876,0.00007069451,0.00004064173],"domain_scores_gemma":[0.9997215,0.0001135335,0.00003104133,0.00004233252,0.00006746678,0.00002419746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007585258,0.0004314411,0.01049588,0.0007051982,0.00007455191,0.0005684613,0.001203395,0.05912764,0.2160087,0.00884286,0.01351323,0.6882701],"study_design_scores_gemma":[0.0001977308,0.003199101,0.02703162,0.0001697459,0.0002494444,0.005405687,0.002142519,0.6160254,0.1298364,0.009450547,0.2061242,0.0001675201],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4864426,0.001915317,0.4549622,0.0006432797,0.0002598287,0.0002686874,0.0002763125,0.003493299,0.05173849],"genre_scores_gemma":[0.8835489,0.0006386315,0.1064285,0.0001774275,0.00005139014,0.00009604752,0.0003238945,0.0001167355,0.008618414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003518073,"threshold_uncertainty_score":0.01176912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02489649259657918,"score_gpt":0.2226532896813089,"score_spread":0.1977567970847297,"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."}}