{"id":"W2292013198","doi":"","title":"ALFA LAVAL S型分油机故障分析及处理","year":2015,"lang":"zh","type":"article","venue":"航海技术","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004898082,0.0002333846,0.0002177847,0.00003477961,0.0001761758,0.00005593966,0.0004258823,0.0001512133,0.02100777],"category_scores_gemma":[0.00004116003,0.0002081226,0.0001062548,0.0003194934,0.0002838791,0.0001832058,0.000146701,0.0002326606,0.009981133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001377626,"about_ca_system_score_gemma":0.00008982433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001876029,"about_ca_topic_score_gemma":0.0001054078,"domain_scores_codex":[0.9979727,0.00005107702,0.0003074831,0.0004736026,0.0006180974,0.0005770849],"domain_scores_gemma":[0.9987134,0.00002419183,0.00006580659,0.000476077,0.00001487741,0.0007056282],"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":[0.0001811223,0.002371495,0.2760754,0.0001095554,0.00006286625,0.0006130649,0.007332208,0.004697739,0.005994607,0.009077357,0.5222384,0.1712462],"study_design_scores_gemma":[0.001331597,0.0003894567,0.0797358,0.000080096,0.00008475229,0.00004832758,0.0007441927,0.00514828,0.0007163321,0.003396427,0.9074918,0.0008329578],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1735069,0.0008982287,0.0004550631,0.002087937,0.0009122704,0.0002597812,0.00004967988,0.00009515746,0.821735],"genre_scores_gemma":[0.9547393,0.0001343398,0.0009428727,0.0003423811,0.0001786576,0.000006143296,0.00001656746,0.0000253479,0.04361437],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7812325,"threshold_uncertainty_score":0.9907897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0226098378294982,"score_gpt":0.2334165489058628,"score_spread":0.2108067110763646,"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."}}