{"id":"W4284678940","doi":"10.2196/preprints.40711","title":"Understanding Homelessness among Migrants to Thunder Bay using Machine Learning (Preprint)","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Homelessness and Social Issues","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Thunder; Bay; Overfitting; Mainstream; Geography; Poverty; Psychology; Demography; Sociology; Political science; Economic growth; Computer science; Artificial intelligence; Economics","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":[0.0006900688,0.000235147,0.0001979813,0.001090062,0.0004464088,0.001449692,0.0003750217,0.000455869,0.003081689],"category_scores_gemma":[0.003582402,0.0001075393,0.0003710951,0.0009562788,0.0002372846,0.0008724694,0.0005337488,0.0004823253,0.0005842258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008626961,"about_ca_system_score_gemma":0.0007228164,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03622606,"about_ca_topic_score_gemma":0.04416414,"domain_scores_codex":[0.9998507,0.00004697369,0.0000166602,0.00003034154,0.00002226086,0.00003292937],"domain_scores_gemma":[0.9986773,0.000751323,0.0002657867,0.00005376264,0.0001583285,0.00009337279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008042152,0.0002385837,0.878436,0.0001696245,0.00005624874,0.0002080679,0.002247249,0.005147076,0.0006252453,0.0005295767,0.008914188,0.1033477],"study_design_scores_gemma":[0.000009479744,0.0001195682,0.9338025,0.00037939,0.00004566336,0.0001052153,0.0107245,0.04822969,0.0005246413,0.001324229,0.00471215,0.00002296227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863578,0.0007118625,0.004214749,0.003088252,0.00006827922,0.00007541441,0.002546192,0.00008549216,0.002852027],"genre_scores_gemma":[0.9901503,0.0006690848,0.004854525,0.0002333634,0.00005646205,0.00008363783,0.002619194,0.000008767823,0.001324733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.963774,"threshold_uncertainty_score":0.07203043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2138476133930158,"score_gpt":0.4255782635238845,"score_spread":0.2117306501308687,"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."}}