{"id":"W1486005927","doi":"","title":"Integrated subsystem for Obstacle detection from a belt of micro-cameras","year":2009,"lang":"en","type":"article","venue":"International Conference on Advanced Robotics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Nautical Research Society","funders":"","keywords":"Computer vision; Occupancy grid mapping; Artificial intelligence; Computer science; Obstacle; Mobile robot; Robot; Pixel; 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":[0.0003830418,0.0007030579,0.0006823833,0.0007500388,0.0002731729,0.000746018,0.001972217,0.0006375524,0.007049051],"category_scores_gemma":[0.0005963916,0.0003997998,0.0004328992,0.0003344899,0.0002013183,0.0008550276,0.0006872787,0.0006614603,0.002153383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006334571,"about_ca_system_score_gemma":0.0008014143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00243052,"about_ca_topic_score_gemma":0.00343242,"domain_scores_codex":[0.9994997,0.00003243674,0.00001746593,0.000112242,0.0002754345,0.00006268897],"domain_scores_gemma":[0.9995775,0.00005583694,0.00003381076,0.00007759174,0.0002049378,0.00005026703],"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":[0.0007941371,0.0004144219,0.00477665,0.0006116068,0.000212083,0.0002635128,0.0002744852,0.01015244,0.4656786,0.003100404,0.005649497,0.5080721],"study_design_scores_gemma":[0.0002439657,0.002627878,0.02524978,0.000193312,0.0004657786,0.001557647,0.000242385,0.4337196,0.4741319,0.001619254,0.0597764,0.0001720359],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04730108,0.0006499564,0.9415071,0.00008143464,0.0001486101,0.0003455647,0.0002074612,0.006284023,0.003474773],"genre_scores_gemma":[0.4569139,0.000529581,0.5271878,0.000207406,0.00009523948,0.0004860047,0.0008300671,0.0002106916,0.01353929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007049051,"threshold_uncertainty_score":0.02358145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05237526766791824,"score_gpt":0.3293643129725612,"score_spread":0.276989045304643,"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."}}