{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002341023,0.0004635356,0.0004944821,0.001219749,0.0003406245,0.0005701535,0.0004235285,0.0005321697,0.0007599319],"category_scores_gemma":[0.0008774687,0.0001804061,0.0004049991,0.001000673,0.0002896324,0.0006383765,0.0003726354,0.0004117733,0.0003313279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003910677,"about_ca_system_score_gemma":0.0004251229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006283527,"about_ca_topic_score_gemma":0.007007683,"domain_scores_codex":[0.9998722,0.00002034434,0.000005558154,0.00003954719,0.00003632403,0.00002592824],"domain_scores_gemma":[0.9996998,0.0001414083,0.00004136823,0.00002111499,0.00007211658,0.00002424646],"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":[0.0006250165,0.0002162199,0.00456697,0.0001268112,0.00007451791,0.0001739686,0.0001814024,0.2907673,0.111654,0.003490652,0.002441488,0.5856816],"study_design_scores_gemma":[0.000004131105,0.00002916029,0.001642595,0.000005535632,0.000008219814,0.00004111385,0.00002567748,0.9888404,0.007533289,0.001470622,0.0003936158,0.000005637932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2411535,0.0005121045,0.7551103,0.000174033,0.00005126604,0.00006578485,0.0003324334,0.000857393,0.001743222],"genre_scores_gemma":[0.7396904,0.0004119141,0.2566502,0.00007399901,0.00005383717,0.00005958288,0.000952549,0.00007177738,0.00203574],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006283527,"threshold_uncertainty_score":0.01249391,"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."}}