{"id":"W2518415677","doi":"10.1017/s0373463316000540","title":"MSARI: A Database for Large Volume Storage and Utilisation of Maritime Data","year":2016,"lang":"en","type":"article","venue":"Journal of Navigation","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Volume (thermodynamics); Situation awareness; Computer science; Data management; Work (physics); Database; Identification (biology); Domain (mathematical analysis); Data science; Engineering","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":[0.007264246,0.00159405,0.002280069,0.006887948,0.001448732,0.007347137,0.006465508,0.001676209,0.01601146],"category_scores_gemma":[0.01904491,0.001309328,0.001130431,0.008475681,0.0008770319,0.00947179,0.005802293,0.002681357,0.02216066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008720925,"about_ca_system_score_gemma":0.003275335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003400999,"about_ca_topic_score_gemma":0.002611591,"domain_scores_codex":[0.9951354,0.0006701825,0.001197205,0.00084807,0.00181437,0.0003348631],"domain_scores_gemma":[0.9825142,0.003008696,0.001561783,0.008079154,0.003263614,0.001572681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001661141,0.0003970928,0.008411005,0.001973253,0.0004316473,0.0008509698,0.001719692,0.005131767,0.02866586,0.03205523,0.5701292,0.348573],"study_design_scores_gemma":[0.0004559836,0.0003265369,0.00912457,0.0003935939,0.0002572939,0.001120718,0.0007020013,0.03731824,0.03471318,0.01856174,0.8965819,0.0004442579],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.01251406,0.00378529,0.4489411,0.001815365,0.0007823733,0.00169842,0.113254,0.3912169,0.02599236],"genre_scores_gemma":[0.1322176,0.004613618,0.4341073,0.001680322,0.001153996,0.002789104,0.3807706,0.02481885,0.01784869],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.01601146,"threshold_uncertainty_score":0.05356365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02488114814046262,"score_gpt":0.2677244014432653,"score_spread":0.2428432533028027,"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."}}