{"id":"W4301895198","doi":"","title":"Dynamic Programming and Skyline Extraction in Catadioptric Infrared Images","year":2009,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Catadioptric system; Skyline; Computer science; Computer vision; Artificial intelligence; Extraction (chemistry); Infrared; Feature extraction; Pattern recognition (psychology); Computer graphics (images); Data mining; Optics; Physics","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":[0.0002821597,0.0004558568,0.0006483021,0.0005925625,0.0002717317,0.0008115809,0.0006154376,0.0004820084,0.00247769],"category_scores_gemma":[0.0008483357,0.0004115045,0.0005139984,0.0007832447,0.0003328162,0.0007532767,0.0005826711,0.0006459158,0.0005762524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003703492,"about_ca_system_score_gemma":0.0004547997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004705917,"about_ca_topic_score_gemma":0.006301617,"domain_scores_codex":[0.9998127,0.00003766916,0.000006151232,0.00005234485,0.00005811196,0.00003298868],"domain_scores_gemma":[0.9997562,0.0001119656,0.00002873942,0.00002453884,0.00005762936,0.00002098867],"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":[0.0003141933,0.00008934106,0.001121,0.0001773696,0.00005118247,0.0001111479,0.000191171,0.4297679,0.06049981,0.008489869,0.003078138,0.4961088],"study_design_scores_gemma":[0.000007192957,0.00001999315,0.0005498699,0.000005737159,0.000005065793,0.00003241608,0.00002510699,0.9907646,0.0057933,0.00186994,0.0009210954,0.000005768448],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03968513,0.0001219026,0.9572631,0.0001261271,0.00001413022,0.00002445383,0.00007248858,0.0006572026,0.002035531],"genre_scores_gemma":[0.3668799,0.0003168559,0.6254483,0.00006770328,0.00004329544,0.00007353251,0.0004042668,0.0005373275,0.006228755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004705917,"threshold_uncertainty_score":0.009357035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00534804811772829,"score_gpt":0.2085418268425837,"score_spread":0.2031937787248554,"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."}}