{"id":"W2143162155","doi":"10.1109/ccece.2004.1349627","title":"Identifying distinguishing size and shape features of mine-like objects in sidescan sonar imagery","year":2004,"lang":"en","type":"article","venue":"","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"U.S. Navy; Defence Research and Development Canada","keywords":"Sonar; Shadow (psychology); Artificial intelligence; Geology; Computer science; Computer vision; Set (abstract data type); Linear discriminant analysis; Pattern recognition (psychology); Feature extraction; Feature (linguistics); Remote sensing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0003083636,0.0002585595,0.0003087022,0.001356235,0.0001826478,0.0004345422,0.0002397154,0.000314933,0.0005805549],"category_scores_gemma":[0.001367741,0.0001437126,0.0002258761,0.0005926908,0.0003320606,0.0005059188,0.0003471174,0.0002316648,0.0003444855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001226954,"about_ca_system_score_gemma":0.000242053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001935355,"about_ca_topic_score_gemma":0.004543297,"domain_scores_codex":[0.9997941,0.00002767456,0.00001321755,0.00003551261,0.00009222452,0.00003724765],"domain_scores_gemma":[0.9990966,0.0003317433,0.0001490202,0.0001088345,0.0002493532,0.00006444723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0006464287,0.0001866771,0.1755253,0.0002436794,0.00005585713,0.0005166387,0.0006317379,0.009478471,0.4084461,0.0005553158,0.001119306,0.4025945],"study_design_scores_gemma":[0.00002979879,0.0003708237,0.7872201,0.00003150569,0.00005796818,0.001703275,0.0009183934,0.1090215,0.09770046,0.0006241243,0.00226082,0.00006128969],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9820415,0.00007756482,0.01593328,0.0000259415,0.000006635309,0.00003093786,0.0002432449,0.0001167519,0.001524076],"genre_scores_gemma":[0.9750032,0.00005563735,0.02380184,0.00001477731,0.000009070999,0.00001897039,0.0006824855,0.00002005393,0.0003939474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001935355,"threshold_uncertainty_score":0.003848195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02398640090928444,"score_gpt":0.2609461328640062,"score_spread":0.2369597319547218,"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."}}