{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001337213,0.0001144178,0.0001444487,0.0001012554,0.00003534477,0.00003758331,0.0001629712,0.00004943413,0.0007399055],"category_scores_gemma":[0.000001214655,0.00009527712,0.00004784507,0.0003329164,0.000005550433,0.0002076403,0.00009788361,0.00005401564,0.000559009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001625648,"about_ca_system_score_gemma":0.000002313765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001430514,"about_ca_topic_score_gemma":0.006110772,"domain_scores_codex":[0.9991278,0.00002494781,0.0001489526,0.000304813,0.0001425483,0.0002509612],"domain_scores_gemma":[0.9995837,0.00002093815,0.00003404292,0.0002443095,0.000002242366,0.000114801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002341435,0.00005417478,0.9866166,0.00002737939,0.000006770716,0.000006778992,0.0004468845,0.009955354,0.001253858,0.00002804757,0.0004665217,0.001114227],"study_design_scores_gemma":[0.001465289,0.0003084604,0.9194465,0.0001065167,0.00001561187,0.000002609111,0.0001360432,0.06357991,0.001264681,0.002149699,0.0109201,0.0006045458],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934264,0.00004580499,0.0001965213,0.0002883272,0.00008258888,0.0003097349,0.000009078215,0.00002786434,0.005613725],"genre_scores_gemma":[0.9980014,0.00001505594,0.0005184645,0.001385205,0.00001636885,0.00002779393,0.00000550843,0.00001109441,0.00001913422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06717005,"threshold_uncertainty_score":0.8101447,"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."}}