{"id":"W4229365934","doi":"10.18174/sesmo.18156","title":"Perspectives on confronting issues of scale in systems modeling","year":2022,"lang":"en","type":"article","venue":"Socio-Environmental Systems Modeling","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Socio-Environmental Synthesis Center; National Science Foundation","keywords":"Scale (ratio); Context (archaeology); Process (computing); Key (lock); Computer science; Environmental systems; Modeling language; Systems modeling; Management science; Data science; Complement (music); Discipline; System dynamics; Systems engineering; Engineering ethics; Knowledge management; Process management; Engineering; Sociology; Ecology; Computer security; Geography; Artificial intelligence; Social science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001046484,0.0003086203,0.000541815,0.0001049059,0.0005294153,0.00005963424,0.0004615279,0.0001055573,0.0004001943],"category_scores_gemma":[0.000004041048,0.0003003503,0.0001424779,0.0001655081,0.00003875004,0.0003379747,0.0004984813,0.000346953,0.0001032996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001253877,"about_ca_system_score_gemma":0.000009856049,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009279029,"about_ca_topic_score_gemma":0.00002192743,"domain_scores_codex":[0.9966901,0.000324073,0.0008093599,0.0007061238,0.0009346076,0.0005357701],"domain_scores_gemma":[0.9991906,0.00003328098,0.000257294,0.0004117452,0.000003294979,0.0001038452],"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":[0.0000304925,0.0001758184,0.1449258,0.00006181237,0.0000222519,0.000005100055,0.007319069,0.8459491,0.001383672,0.00007162691,0.00001788741,0.00003731261],"study_design_scores_gemma":[0.0003826406,0.00007932802,0.0001044963,0.0000867468,0.00001381108,0.000009844556,0.2995129,0.699407,0.000005460386,0.00005001891,0.00009296968,0.0002547368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931949,0.002893614,0.001065955,0.00005814061,0.0004228203,0.0006831931,0.00006408177,0.00005489188,0.001562431],"genre_scores_gemma":[0.9991983,0.0001493605,0.00008609108,0.00002122402,0.0001134213,0.0001918438,0.00002695,0.00005493225,0.0001578876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2921938,"threshold_uncertainty_score":0.9999449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0133008360283434,"score_gpt":0.2159802685476198,"score_spread":0.2026794325192764,"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."}}