{"id":"W3163584383","doi":"10.1109/lgrs.2021.3076661","title":"Fast Ship Detection With Spatial-Frequency Analysis and ANOVA-Based Feature Fusion","year":2021,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Windsor; Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Clutter; Computer science; Artificial intelligence; Pattern recognition (psychology); Radar; Feature extraction; Feature (linguistics); Doppler effect; Time–frequency analysis; Classifier (UML); Fusion; Algorithm; Computer vision; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009884438,0.0008553304,0.001156588,0.001646652,0.0003509685,0.0007182566,0.0007569437,0.0006285099,0.0005884826],"category_scores_gemma":[0.002084081,0.0002802011,0.001245101,0.001427732,0.0003885566,0.001135372,0.001179264,0.0008863739,0.0005115726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000292403,"about_ca_system_score_gemma":0.0005772127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001476566,"about_ca_topic_score_gemma":0.001577121,"domain_scores_codex":[0.9991376,0.0001369548,0.00005759053,0.0002567259,0.0003130601,0.00009798348],"domain_scores_gemma":[0.9992004,0.0002575476,0.0001020624,0.0001401386,0.0002655348,0.00003430057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002553083,0.0001849466,0.004076263,0.00006927195,0.0001740275,0.0001231843,0.00009892863,0.06669348,0.0561337,0.002623754,0.002139316,0.8674278],"study_design_scores_gemma":[0.00001259595,0.0001431074,0.003780924,0.000004837277,0.00004401206,0.0001680839,0.00002798642,0.9783456,0.01383452,0.002390933,0.001220576,0.00002676296],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03642522,0.000200999,0.9618173,0.00007115571,0.00004415614,0.00002185379,0.00007615882,0.0008441915,0.0004989594],"genre_scores_gemma":[0.5334988,0.0002506646,0.4636744,0.0001005787,0.0001194905,0.00007253423,0.0007530577,0.00008911268,0.001441246],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001646652,"threshold_uncertainty_score":0.005227447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005738087095155151,"score_gpt":0.2047094609390232,"score_spread":0.1989713738438681,"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."}}