{"id":"W2234301625","doi":"10.1007/978-3-319-12307-3_55","title":"Evolutionary Multiobjective Optimization (EOM) Design for Peri-urban Greenlands Systems: Metric Implementations","year":2015,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Esri (Canada); Wilfrid Laurier University","funders":"","keywords":"Implementation; Metric (unit); Computer science; Multi-objective optimization; Mathematical optimization; Mathematics; Engineering; Software engineering; Operations management","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":[0.001238023,0.001130681,0.001047548,0.0006802324,0.0004366846,0.001197358,0.001173387,0.00146936,0.004784196],"category_scores_gemma":[0.002379229,0.0005672719,0.0009829066,0.0008852046,0.0005225824,0.001002091,0.001546119,0.001218317,0.0004364896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008073925,"about_ca_system_score_gemma":0.000983068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003203648,"about_ca_topic_score_gemma":0.004362343,"domain_scores_codex":[0.9996492,0.0001647208,0.00001849242,0.00004889755,0.00008624509,0.00003251748],"domain_scores_gemma":[0.9993456,0.0004234173,0.00004863087,0.00003674921,0.0001154431,0.00003019318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001136024,0.00001855425,0.0001280924,0.00006749362,0.00001833431,0.00001940168,0.00002051392,0.9626524,0.0003544895,0.009993688,0.0004342397,0.02628142],"study_design_scores_gemma":[0.000004902715,0.00001928444,0.00004903315,0.0000125592,0.000004223096,0.000008243305,0.00001012221,0.9935748,0.0001236886,0.005316452,0.0008740393,0.000002693002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009250757,0.0005771499,0.980755,0.000198083,0.00004477975,0.00005767248,0.00006724455,0.00009665344,0.008952744],"genre_scores_gemma":[0.4052642,0.0009722784,0.5833629,0.0001796166,0.00006019427,0.0004756372,0.0002080462,0.0001806187,0.009296423],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004784196,"threshold_uncertainty_score":0.01600474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09707432271384445,"score_gpt":0.2914406749350711,"score_spread":0.1943663522212267,"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."}}