{"id":"W4376134160","doi":"10.1109/esars-itec57127.2023.10114836","title":"Water Transport Decarbonization: Preliminary Case Study in Venice","year":2023,"lang":"en","type":"article","venue":"","topic":"Maritime Ports and Logistics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Electrification; Taxis; Public transport; Water transport; Electricity; Transport engineering; Energy consumption; Service (business); Water consumption; Environmental science; Civil engineering; Computer science; Engineering; Environmental engineering; Business; Water flow; Electrical 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":[0.0004582314,0.0005810632,0.0004158316,0.0008320799,0.001558902,0.001374995,0.001038942,0.001341671,0.003050486],"category_scores_gemma":[0.0009081738,0.0001974917,0.0006562218,0.001734359,0.000851194,0.0009232817,0.001036284,0.0008763812,0.0002492905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002738947,"about_ca_system_score_gemma":0.001474651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09616882,"about_ca_topic_score_gemma":0.1183464,"domain_scores_codex":[0.9994992,0.0001478125,0.00002074652,0.00007299712,0.00006997553,0.0001893049],"domain_scores_gemma":[0.9994709,0.0001891355,0.00004749494,0.00006563677,0.00008896423,0.0001378592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0008080898,0.001691573,0.1009768,0.0008099186,0.000190636,0.04880831,0.0032864,0.741829,0.01154124,0.02743544,0.008501586,0.05412107],"study_design_scores_gemma":[0.0002879624,0.001794545,0.1783214,0.000402754,0.0002632985,0.004856931,0.0467848,0.6434423,0.03322166,0.00935753,0.08100303,0.0002637361],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9751647,0.0002562841,0.003702152,0.0002468535,0.00003173453,0.0001911259,0.0004501458,0.0001020682,0.01985504],"genre_scores_gemma":[0.9930911,0.0002554611,0.001908344,0.0000344662,0.000007264254,0.00003286433,0.0002719579,0.00002749038,0.004371063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09616882,"threshold_uncertainty_score":0.1912181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0191431817494398,"score_gpt":0.233047282860514,"score_spread":0.2139041011110742,"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."}}