{"id":"W1979804875","doi":"10.1109/oceans.2014.7003300","title":"Ship performance monitoring and analysis to improve fuel efficiency","year":2014,"lang":"en","type":"article","venue":"","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Fuel efficiency; Performance indicator; Payload (computing); Baseline (sea); Computer science; Key (lock); Automotive engineering; Environmental science; Engineering; Computer security","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":[0.001790808,0.0008962802,0.0007145836,0.002735128,0.0003299133,0.001166041,0.000721142,0.0002771473,0.003761874],"category_scores_gemma":[0.002923254,0.0002705715,0.0005079492,0.003512799,0.0001113873,0.001109406,0.0006256781,0.0005662901,0.001518542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008145905,"about_ca_system_score_gemma":0.0009453705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01145778,"about_ca_topic_score_gemma":0.0144702,"domain_scores_codex":[0.9989374,0.0001529524,0.00006573871,0.0002376517,0.0005308203,0.00007531721],"domain_scores_gemma":[0.9974909,0.000324185,0.0002359468,0.000352613,0.001532828,0.00006357741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006762662,0.001221986,0.2083985,0.0007186494,0.0002758047,0.000179668,0.0004831729,0.0797845,0.05591158,0.001556443,0.01899169,0.6318018],"study_design_scores_gemma":[0.00008398029,0.001487154,0.5010209,0.0001145949,0.0001522445,0.0001236314,0.0006161583,0.3296322,0.1167744,0.001615167,0.0482434,0.0001362725],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7278402,0.0003192102,0.2074135,0.0003864695,0.00011835,0.001264139,0.02825801,0.00585724,0.02854278],"genre_scores_gemma":[0.8218012,0.0004087528,0.1363987,0.00008294788,0.00003921829,0.000635686,0.02663937,0.000617372,0.01337672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01145778,"threshold_uncertainty_score":0.02278215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005842463136903618,"score_gpt":0.2087627781299774,"score_spread":0.2029203149930738,"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."}}