{"id":"W2117572508","doi":"10.1145/1815396.1815576","title":"Continuing progress in augmenting urban search and rescue dogs","year":2010,"lang":"en","type":"article","venue":"","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Search and rescue; Computer science; Artificial intelligence","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":[0.002246064,0.0004573417,0.0002822509,0.0005799903,0.0002739869,0.001188832,0.001011912,0.001045972,0.008307022],"category_scores_gemma":[0.001664546,0.0001552189,0.000343603,0.0008330393,0.0006518013,0.002307021,0.0006180117,0.00042306,0.001728109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002977602,"about_ca_system_score_gemma":0.0007411834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002536682,"about_ca_topic_score_gemma":0.002620413,"domain_scores_codex":[0.9994843,0.0001608731,0.00002902258,0.0000926739,0.000181174,0.0000518711],"domain_scores_gemma":[0.9987882,0.0003721945,0.00007213029,0.0001725641,0.00048991,0.0001050375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001819731,0.0003947609,0.00325493,0.001149685,0.00003265098,0.00009442595,0.0008189889,0.007768697,0.02821732,0.008912757,0.01136121,0.9378126],"study_design_scores_gemma":[0.0001092849,0.002714038,0.01310444,0.0005844698,0.0001203369,0.001069593,0.002999683,0.02966083,0.0420358,0.005657889,0.9018669,0.00007669855],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3705252,0.09256558,0.3778877,0.01758875,0.001144971,0.0004057412,0.0005728229,0.00333976,0.1359695],"genre_scores_gemma":[0.5531805,0.05956008,0.3335404,0.001551628,0.0005733321,0.0002076204,0.001329117,0.0002423111,0.04981488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008307022,"threshold_uncertainty_score":0.02778971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009406963764235193,"score_gpt":0.2390268970670489,"score_spread":0.2296199333028137,"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."}}