{"id":"W2583902900","doi":"","title":"Looking Forward to Better Feauture Detection","year":2015,"lang":"en","type":"article","venue":"The International Hydrographic Review","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Hydrographic Service","funders":"","keywords":"Navy; Hydrography; Sonar; Perspective (graphical); Feature (linguistics); Hydrographic survey; Computer science; Service (business); Aeronautics; Engineering; Operations research; Remote sensing; Cartography; Geography; Artificial intelligence; Business","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":[0.00605541,0.0009329875,0.001091604,0.002342646,0.0006382828,0.002831373,0.002540777,0.003245758,0.01224514],"category_scores_gemma":[0.009304684,0.0005036038,0.000947193,0.001342576,0.001319033,0.009379626,0.001394418,0.002385066,0.005964661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001671397,"about_ca_system_score_gemma":0.002666831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01163495,"about_ca_topic_score_gemma":0.01704785,"domain_scores_codex":[0.9979541,0.0003386595,0.00009515371,0.0004331823,0.0009610404,0.0002178435],"domain_scores_gemma":[0.9915532,0.001588077,0.0005792369,0.0004210768,0.005607599,0.0002507074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006304184,0.000065689,0.00515346,0.002139844,0.00009024575,0.00012749,0.0004168097,0.001671251,0.008393656,0.02666068,0.05350841,0.9017093],"study_design_scores_gemma":[0.00002217561,0.0002669214,0.00727316,0.001725494,0.00009748741,0.0007032086,0.00114438,0.003664924,0.00851579,0.01348659,0.9629876,0.0001121271],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.02496019,0.4856973,0.2542366,0.1427808,0.01197473,0.0001831272,0.0004756988,0.00203987,0.07765158],"genre_scores_gemma":[0.1992604,0.3796681,0.2588859,0.03247595,0.004484766,0.0001733391,0.001495874,0.0006533808,0.1229023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01224514,"threshold_uncertainty_score":0.04096413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02219061450383843,"score_gpt":0.2542302181603177,"score_spread":0.2320396036564793,"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."}}