{"id":"W2189223122","doi":"","title":"OBJECT-BASED MOVING VEHICLE EXTRACTION FROM WORLDVIEW2 IMAGERY","year":2012,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Remote sensing; Feature extraction; Object detection; Extraction (chemistry); Satellite; Geography; Pattern recognition (psychology); Engineering","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":[0.000140256,0.0001231702,0.0001075473,0.00008065535,0.00004601935,0.00005643554,0.0000599786,0.00006364429,0.0002397353],"category_scores_gemma":[0.00005553877,0.0001265603,0.00004922419,0.0001560962,0.00001818815,0.0005019368,0.000008203106,0.0001488558,0.0006726863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000178801,"about_ca_system_score_gemma":0.00001302177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000905202,"about_ca_topic_score_gemma":0.00001365153,"domain_scores_codex":[0.999291,0.00002781935,0.000182744,0.000124285,0.0001142114,0.0002599492],"domain_scores_gemma":[0.9994241,0.0001333527,0.00003083834,0.0002946628,0.00002939066,0.0000876527],"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":[0.000003833951,0.00002377812,0.00267931,0.00001743203,0.00001318414,0.000001399644,0.00007791668,0.003758117,0.9467585,0.00001913999,0.004073809,0.04257355],"study_design_scores_gemma":[0.0002156375,0.000005290245,0.1154084,0.00003549997,0.00002452355,0.000002676141,0.00006585178,0.5408322,0.3325761,0.00003696542,0.01050839,0.0002884764],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.686749,0.0005512034,0.2789282,0.0001536531,0.001300852,0.0001533778,0.000003899534,0.001343182,0.0308166],"genre_scores_gemma":[0.9767583,0.000009406812,0.02241316,0.00008660382,0.0003150395,0.000003385024,0.00002290264,0.00004334203,0.0003478737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6141825,"threshold_uncertainty_score":0.8646246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01817543215627145,"score_gpt":0.2394291814509872,"score_spread":0.2212537492947157,"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."}}