{"id":"W3080598297","doi":"10.5194/isprs-archives-xliii-b3-2020-1185-2020","title":"A COMPUTER VISION APPROACH FOR DETECTION OF ASTEROIDS/COMETS IN SPACE SATELLITE IMAGES","year":2020,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Astro and Planetary Science","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canadian Space Agency","keywords":"Asteroid; Computer science; Software; Comet; Trajectory; Computer vision; Brightness; Satellite; Laptop; Spacecraft; Computer graphics (images); Pixel; Artificial intelligence; Path (computing); Remote sensing; Geography; Astronomy; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004840161,0.0005398438,0.0004321235,0.002627478,0.0005246296,0.0008092857,0.0006236424,0.001039834,0.001423466],"category_scores_gemma":[0.0007395381,0.0002663349,0.000788967,0.0009644162,0.0003993284,0.000574744,0.0004445172,0.0005873033,0.0005529607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006976929,"about_ca_system_score_gemma":0.0009704932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00811155,"about_ca_topic_score_gemma":0.007965468,"domain_scores_codex":[0.9996142,0.00004799458,0.0000212074,0.0001401801,0.0001225488,0.00005379005],"domain_scores_gemma":[0.9997079,0.00005562402,0.00003543496,0.00002335644,0.000152728,0.00002489191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002123281,0.000374937,0.004700975,0.0002284714,0.0002026364,0.0003536721,0.0002007879,0.08026031,0.09674701,0.006705085,0.005031982,0.8049818],"study_design_scores_gemma":[0.00002089743,0.000130927,0.003765697,0.00001739906,0.00004061499,0.0002615763,0.00007751392,0.9734301,0.01672949,0.002579025,0.002922735,0.00002406342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04359835,0.0005681033,0.952,0.0001772474,0.0001037387,0.0001554714,0.00008143872,0.0009498057,0.002365808],"genre_scores_gemma":[0.3742315,0.0004861675,0.6209719,0.0001792858,0.00007922461,0.0001723938,0.0002999808,0.00004814646,0.003531358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00811155,"threshold_uncertainty_score":0.01612866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01468912238392206,"score_gpt":0.2420435549782947,"score_spread":0.2273544325943727,"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."}}