{"id":"W4300666872","doi":"","title":"Gas Plume Detection and Tracking in Hyperspectral Video Sequences using Binary Partition Trees","year":2014,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Hyperspectral imaging; Tracking (education); Plume; Partition (number theory); Computer science; Binary number; Artificial intelligence; Computer vision; Remote sensing; Geology; Mathematics; Meteorology; Geography; Combinatorics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001734627,0.0001494335,0.0001550489,0.0001912065,0.0001600762,0.0001882369,0.000149759,0.00009537026,0.000009722275],"category_scores_gemma":[0.0005160451,0.0001718123,0.00004103969,0.000355695,0.000127025,0.0003697689,0.00003647494,0.0001935388,0.0000078301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001514069,"about_ca_system_score_gemma":0.00001984684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003761546,"about_ca_topic_score_gemma":0.00202339,"domain_scores_codex":[0.9980123,0.001030753,0.0002767538,0.0002924117,0.0001576526,0.0002301303],"domain_scores_gemma":[0.9987855,0.000381707,0.00008811919,0.0004312184,0.0002410685,0.00007239474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005311792,0.00005342791,0.002375489,0.00004553298,0.00001036563,0.000002328352,0.002686112,0.003939922,0.9004463,0.00193309,0.00001198567,0.08849015],"study_design_scores_gemma":[0.0002449977,5.273179e-7,0.02695761,0.0003221943,0.00001140987,0.00002112339,0.0001679393,0.7519168,0.2191163,0.0007220544,0.0003391115,0.0001799444],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8843989,0.0002395289,0.1102857,0.0007198235,0.00007753318,0.0001251825,0.00000174493,0.0002418408,0.003909725],"genre_scores_gemma":[0.9817762,0.00014695,0.0179146,0.00001256922,0.00002044034,0.000005295336,0.00001612448,0.00002857277,0.0000792795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7479768,"threshold_uncertainty_score":0.7006302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01744159864490559,"score_gpt":0.2216728690534101,"score_spread":0.2042312704085045,"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."}}