{"id":"W2108512920","doi":"10.1177/154193120805201815","title":"Effects of Automated Scanning in a Search and Rescue Detection Task","year":2008,"lang":"en","type":"article","venue":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Terrain; Computer science; Computer vision; Visibility; Joystick; Artificial intelligence; Search and rescue; Urban search and rescue; Operator (biology); Automation; Motion detection; Real-time computing; Simulation; Mobile robot; Motion (physics); Robot; Engineering; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0008731809,0.00109791,0.0005592672,0.0004909412,0.0003859483,0.0009624321,0.0006046526,0.001170541,0.004276976],"category_scores_gemma":[0.01680505,0.0006505061,0.0003337207,0.0002374953,0.000477625,0.0008444746,0.001077201,0.0006672757,0.0005410812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002835863,"about_ca_system_score_gemma":0.0005854481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002499071,"about_ca_topic_score_gemma":0.001801376,"domain_scores_codex":[0.9989266,0.0004017324,0.0001270527,0.0001619158,0.000214367,0.0001684026],"domain_scores_gemma":[0.9857457,0.01132563,0.001027563,0.000572286,0.0006580043,0.0006708482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.07155782,0.01359523,0.01783241,0.001265266,0.000387056,0.000840061,0.004335816,0.03168298,0.6602648,0.0004176748,0.001817754,0.1960031],"study_design_scores_gemma":[0.007188719,0.1347346,0.4823159,0.0004315655,0.001556451,0.001723009,0.004850243,0.1592085,0.195967,0.002733364,0.00860684,0.0006838756],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973516,0.00006321705,0.001341663,0.00004872895,0.00003042301,0.00006556029,0.00004999383,0.000104527,0.0009441969],"genre_scores_gemma":[0.9921564,0.0001396135,0.00563952,0.0001402098,0.00005422887,0.0001662662,0.0001959089,0.00008156811,0.001426231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004276976,"threshold_uncertainty_score":0.01430786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01748309202741132,"score_gpt":0.2884426950511253,"score_spread":0.270959603023714,"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."}}