{"id":"W2967259835","doi":"10.1111/ecog.04516","title":"NetLogoR: a package to build and run spatially explicit agent‐based models in R","year":2019,"lang":"en","type":"article","venue":"Ecography","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"Agence Nationale de la Recherche","keywords":"NetLogo; Computer science; Context (archaeology); Software; R package; Programming language; Function (biology); Population; Software package; Theoretical computer science; Software engineering; Geography; Biology","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":[0.00444573,0.002829855,0.003261442,0.001884194,0.0008135744,0.003092082,0.00428568,0.001621621,0.1065075],"category_scores_gemma":[0.01598169,0.002456865,0.003538565,0.001284101,0.0008153508,0.002631309,0.002821247,0.003876127,0.06528758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001073445,"about_ca_system_score_gemma":0.002821482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006946337,"about_ca_topic_score_gemma":0.008314745,"domain_scores_codex":[0.9980823,0.0008564109,0.0001565386,0.0004554795,0.0003264306,0.0001228222],"domain_scores_gemma":[0.99408,0.004249807,0.000364432,0.0006840445,0.0004343836,0.00018732],"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":[0.0006152514,0.00009648163,0.005890628,0.004869869,0.002724875,0.0005343755,0.0004789705,0.05864184,0.003956186,0.04085498,0.8029287,0.07840787],"study_design_scores_gemma":[0.0006516101,0.0001454176,0.003170373,0.0005576569,0.0009303828,0.0005292927,0.00009682412,0.1379464,0.004825329,0.09038845,0.760367,0.0003911968],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.002926906,0.001331869,0.5971663,0.001147406,0.000741871,0.0003852042,0.1136327,0.2737793,0.008888417],"genre_scores_gemma":[0.04036472,0.002035783,0.6128211,0.002084187,0.0003791205,0.005282182,0.1027086,0.2176257,0.01669868],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.1065075,"threshold_uncertainty_score":0.3563031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008497111419494476,"score_gpt":0.1975501480235886,"score_spread":0.1890530366040941,"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."}}