{"id":"W7096125270","doi":"","title":"Automated Image Georeference and Tactical Support to Canadian Coast Guard Using Downlink IceVu Systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Environmental Monitoring and Data Management","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Coast guard; Automatic Identification System; Guard (computer science); Image processing; Automation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000461296,0.0002857859,0.0002608892,0.001168589,0.001572968,0.001268357,0.0006326703,0.0002762013,0.003857267],"category_scores_gemma":[0.00180582,0.0002145737,0.0001191369,0.001045829,0.0003090129,0.0005496971,0.0005211984,0.0003704883,0.0009352249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003512671,"about_ca_system_score_gemma":0.008214529,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.811581,"about_ca_topic_score_gemma":0.8786913,"domain_scores_codex":[0.9995433,0.00003903509,0.00001373271,0.00005015322,0.0002082386,0.0001455126],"domain_scores_gemma":[0.9986749,0.00010506,0.00005919554,0.0001171496,0.0009525173,0.00009114968],"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":[0.0007540645,0.0001676683,0.08451749,0.0001198332,0.0000615978,0.000291156,0.002243623,0.05342326,0.1106426,0.004352436,0.09399907,0.6494272],"study_design_scores_gemma":[0.0002090764,0.0001469778,0.1511053,0.00009866516,0.0001469961,0.000149247,0.004353113,0.6492521,0.04881126,0.00159495,0.1440072,0.0001251798],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8415335,0.0003906034,0.07311306,0.00132262,0.0003169841,0.0004092829,0.006820543,0.00852395,0.06756946],"genre_scores_gemma":[0.9239569,0.0001943189,0.05709941,0.0001697931,0.00004782283,0.00008845504,0.004938823,0.00028174,0.01322269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.188419,"threshold_uncertainty_score":0.3790573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03198067944590185,"score_gpt":0.2253675702959407,"score_spread":0.1933868908500389,"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."}}