{"id":"W2112425134","doi":"10.1109/oceans.2008.5151909","title":"Reconstruction and fusion of perceptual features for automatic classification of sonar echoes","year":2008,"lang":"en","type":"article","venue":"","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"Defence Research and Development Canada; Pennsylvania State University","keywords":"Sonar; Computer science; Bandwidth (computing); Artificial intelligence; Marine mammals and sonar; Clutter; Sonar signal processing; Speech recognition; Underwater; Pattern recognition (psychology); Computer vision; Radar; Signal processing; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001190935,0.00004198907,0.00008912003,0.00008091141,0.00007046207,0.000005538889,0.00005135193,0.00004388233,0.0005412175],"category_scores_gemma":[0.00003527803,0.00003071581,0.00001905133,0.00006441365,0.0001702959,0.00008975805,0.000004263739,0.00003538438,0.00000307312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001926941,"about_ca_system_score_gemma":0.00003199152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003792446,"about_ca_topic_score_gemma":0.0003588173,"domain_scores_codex":[0.999514,0.00002452928,0.000135866,0.00009635668,0.0001423427,0.00008689165],"domain_scores_gemma":[0.9996241,0.0001683328,0.00004571126,0.0000630351,0.00006553446,0.00003326036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00006722927,0.00002703667,0.4684791,0.0002162713,0.00001831348,5.867554e-7,0.001347342,0.0005722294,0.05740247,0.00005491899,0.0007531149,0.4710613],"study_design_scores_gemma":[0.0001526114,0.000145338,0.7965151,0.00001003802,0.000005138581,0.00004014202,0.000600114,0.1974485,0.004583136,0.0004385676,0.00002288356,0.00003843201],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920354,0.00005094233,0.006163185,0.00006188231,0.00003207732,0.0001612975,0.00003082855,0.00001270044,0.001451639],"genre_scores_gemma":[0.9735174,0.0001006583,0.02605026,0.00000461554,0.0000150384,7.016323e-7,0.00002757207,0.000001244726,0.000282495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4710229,"threshold_uncertainty_score":0.5925952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04547713100042073,"score_gpt":0.2598942630847377,"score_spread":0.214417132084317,"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."}}