{"id":"W4379016503","doi":"10.21203/rs.3.rs-2986170/v1","title":"MicSim, a microsimulation model for population dynamics","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Microsimulation; Probabilistic logic; Computer science; Context (archaeology); Population; Scale (ratio); Econometrics; Statistical model; European union; Operations research; Regional science; Software; Data science; Geography; Artificial intelligence; Economics; Engineering; Sociology; Cartography; Transport engineering","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":[0.0008050526,0.0006670592,0.0006707042,0.0006931073,0.0004551998,0.0009576141,0.001307179,0.0009220634,0.005469752],"category_scores_gemma":[0.003417213,0.0004567012,0.001274513,0.0007187825,0.0004298827,0.0006868506,0.0008595855,0.0009867967,0.0007701779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008478408,"about_ca_system_score_gemma":0.001412853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01996038,"about_ca_topic_score_gemma":0.009621963,"domain_scores_codex":[0.9996859,0.0001205465,0.00002739878,0.00006636405,0.00006015205,0.00003967316],"domain_scores_gemma":[0.9985716,0.0009260107,0.0001354593,0.00009697371,0.0001936841,0.00007621331],"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":[0.0000263335,0.00001809888,0.001593215,0.00003657714,0.00003768299,0.00004057569,0.00004368452,0.9821149,0.000380087,0.01064767,0.001037923,0.004023187],"study_design_scores_gemma":[0.000006616571,0.000006778106,0.0001482853,0.000005246523,0.000005910278,0.000009768257,0.000006670794,0.9948812,0.0001503166,0.00283986,0.001935021,0.000004302874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0721795,0.00032717,0.9090349,0.0005107635,0.0001562026,0.0001403808,0.004976207,0.004000413,0.008674356],"genre_scores_gemma":[0.7145511,0.0005990275,0.2682908,0.0002276348,0.0001034087,0.0009897905,0.005016189,0.0009508902,0.009271074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01996038,"threshold_uncertainty_score":0.03968841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5088289461810432,"score_gpt":0.5609895856044942,"score_spread":0.05216063942345095,"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."}}