{"id":"W2591592009","doi":"10.1109/antem.2004.7860619","title":"GPR target detection using a neural network classifier of image moments as invariant features","year":2004,"lang":"en","type":"article","venue":"","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Unexploded ordnance; Ground-penetrating radar; Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Computer science; Clutter; Invariant (physics); Classifier (UML); Computer vision; Contextual image classification; Radar; Image (mathematics); Remote sensing; Geology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004933644,0.0004142204,0.0003614163,0.0005220544,0.0001891827,0.0004819687,0.0003601934,0.0007999315,0.0009332724],"category_scores_gemma":[0.001376767,0.0001983577,0.000247628,0.0003346794,0.0001643951,0.0005122228,0.0002420818,0.0004542016,0.0003831473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003492789,"about_ca_system_score_gemma":0.0002335561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001943089,"about_ca_topic_score_gemma":0.002114389,"domain_scores_codex":[0.9998167,0.00003115411,0.00001080667,0.00005354108,0.00005371389,0.0000340283],"domain_scores_gemma":[0.9994654,0.000220975,0.00004789864,0.00003801923,0.0002096585,0.00001805864],"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":[0.0010035,0.0005082224,0.009902772,0.0001233673,0.0001125769,0.0002296845,0.00006581966,0.1453585,0.1179438,0.001380846,0.003091219,0.7202796],"study_design_scores_gemma":[0.00001497556,0.0001272683,0.002496064,0.000008014884,0.0000217385,0.00007047037,0.000008512058,0.9827909,0.01384799,0.0002512968,0.0003526216,0.00001013496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4269202,0.0005832426,0.5640551,0.0002938103,0.0001795367,0.0001406983,0.0002213543,0.002176688,0.005429293],"genre_scores_gemma":[0.8317562,0.0001882262,0.1626617,0.0001094821,0.00004920051,0.000119742,0.0002662274,0.00002996736,0.004819301],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001943089,"threshold_uncertainty_score":0.003863573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0151252897168599,"score_gpt":0.260489000092281,"score_spread":0.2453637103754212,"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."}}