{"id":"W2384081408","doi":"10.1088/1755-1315/34/1/012030","title":"Using locality-constrained linear coding in automatic target detection of HRS images","year":2016,"lang":"en","type":"article","venue":"IOP Conference Series Earth and Environmental Science","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Histogram; Locality; Computer vision; Image (mathematics)","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":[0.0003586357,0.0001252071,0.0001616357,0.0001015607,0.0001412789,0.00005712792,0.000294633,0.00003634425,0.00005255534],"category_scores_gemma":[0.00006238461,0.00009403297,0.00002217993,0.0003142259,0.00141319,0.002125398,0.0001920229,0.00006573618,0.000003909725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004353679,"about_ca_system_score_gemma":0.00005702649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003355895,"about_ca_topic_score_gemma":0.000007067897,"domain_scores_codex":[0.9988025,0.00003586421,0.0002372355,0.0003634449,0.0002659301,0.0002949632],"domain_scores_gemma":[0.9995368,0.00003792396,0.00009049285,0.0002188194,0.00001577694,0.0001002429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000004546795,0.000009971758,0.000572837,0.000007443966,7.551527e-7,0.000004143279,0.00009144637,0.000007825538,0.7563316,0.0003281781,7.510337e-8,0.2426411],"study_design_scores_gemma":[0.0001700555,0.0001320807,0.01344051,0.00007490767,0.00000139699,0.00003651837,0.0001103302,0.01619324,0.9683748,0.001275928,0.00004976052,0.0001405024],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6078159,0.00003494019,0.3918089,0.00005326741,0.00002724612,0.00008931977,0.000006030103,0.00003891776,0.0001255607],"genre_scores_gemma":[0.9379177,0.0002018479,0.06179255,0.00002277683,0.000006681356,0.000002931326,2.962362e-7,0.000003282001,0.00005191326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3301019,"threshold_uncertainty_score":0.5206957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02033978007119445,"score_gpt":0.2543998567187509,"score_spread":0.2340600766475565,"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."}}