{"id":"W2902315198","doi":"10.3390/w10121786","title":"Serious Games as Planning Support Systems: Learning from Playing Maritime Spatial Planning Challenge 2050","year":2018,"lang":"en","type":"article","venue":"Water","topic":"Coastal and Marine Management","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Promotion (chess); Process (computing); Marine spatial planning; Computer science; Knowledge management; Operations research; Process management; Data science; Engineering; Geography; Environmental planning; Political science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002062374,0.0001912262,0.0001801236,0.00003967297,0.0002794098,0.0001292347,0.0002050496,0.00006903046,0.006685749],"category_scores_gemma":[0.00001011855,0.0001473664,0.00004284904,0.00003936442,0.00009335858,0.0002213256,0.00201683,0.0001869432,0.004776308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007581174,"about_ca_system_score_gemma":0.000002819668,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01113098,"about_ca_topic_score_gemma":0.0002081853,"domain_scores_codex":[0.9985251,0.00005283224,0.0002199167,0.0004149438,0.000305944,0.0004812677],"domain_scores_gemma":[0.9996238,0.00002035224,0.00005245343,0.0001944616,0.000006181353,0.0001027928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007852347,0.0004226489,0.4656138,0.000237083,0.0004665409,0.003275806,0.06838574,0.03233423,0.03978655,0.0001958248,0.0642909,0.3242056],"study_design_scores_gemma":[0.001029857,0.0008572196,0.06538778,0.0001729538,0.00006592769,0.00004499768,0.002112268,0.01309206,0.002162946,0.0007228014,0.9134517,0.0008994915],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7743353,0.00003454445,0.0007974206,0.0003007605,0.0006513688,0.0002293684,0.000003841172,0.0001653316,0.223482],"genre_scores_gemma":[0.9867342,0.000004414087,0.0001248769,0.0001633002,0.0004192355,0.0000236787,0.00009321357,0.00002690984,0.01241022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8491608,"threshold_uncertainty_score":0.9959986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01344330629836277,"score_gpt":0.2249682854425518,"score_spread":0.211524979144189,"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."}}