{"id":"W7065677169","doi":"","title":"Employing the Characterization and Parameterization framework (CAP) to develop an empirical innovation-diffusion agent-based model of green infrastructure adoption on private residential yards.","year":2022,"lang":"en","type":"article","venue":"ScholarsArchive  (Brigham Young University)","topic":"Magnetic confinement fusion research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scale (ratio); Empirical research; Field (mathematics); Multinomial logistic regression; Identification (biology); Measure (data warehouse)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002014976,0.0008880571,0.0009388819,0.001053556,0.0005699332,0.002460177,0.002293131,0.002620116,0.006691393],"category_scores_gemma":[0.006011625,0.0005773294,0.001593022,0.001460304,0.0009973461,0.0027853,0.001351014,0.002364084,0.0009360749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002910498,"about_ca_system_score_gemma":0.001757902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03106474,"about_ca_topic_score_gemma":0.02236384,"domain_scores_codex":[0.9991135,0.0003921727,0.00003941203,0.0002240546,0.0001146664,0.0001160904],"domain_scores_gemma":[0.9982174,0.001035967,0.0002979549,0.0001094117,0.0002425907,0.00009656469],"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":[0.00002719382,0.0001052243,0.006180663,0.00008033981,0.00006801388,0.0001985498,0.0002962864,0.8463029,0.0006133708,0.1358149,0.002023627,0.008288905],"study_design_scores_gemma":[0.00001254206,0.00002406753,0.0007784271,0.00001153252,0.00001708715,0.00004126421,0.00007224112,0.9683892,0.0000676087,0.0279829,0.002588142,0.0000150493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09225451,0.0005873233,0.8708954,0.00340211,0.0001029536,0.0003514735,0.001390125,0.0002944496,0.03072163],"genre_scores_gemma":[0.8850713,0.0006649347,0.08475783,0.00032615,0.00008079364,0.0009138812,0.0009835109,0.00007484122,0.02712674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03106474,"threshold_uncertainty_score":0.06176788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02124525703745372,"score_gpt":0.259187946897251,"score_spread":0.2379426898597973,"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."}}