{"id":"W7116054486","doi":"10.11575/prism/50841","title":"Serving More People Than You Can Tax: The Fiscal Impact of “Fringe Populations” on Northern Ontario Municipalities","year":2025,"lang":"en","type":"other","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Per capita; Population; Population size; Recreation; Per capita income","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":[0.0008562767,0.0001750979,0.0002927651,0.001266841,0.002285627,0.00213195,0.0009841481,0.0003443835,0.005860878],"category_scores_gemma":[0.006248788,0.0001783682,0.000526165,0.00347188,0.001011451,0.0007153964,0.001716551,0.0004867238,0.0002579666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0369696,"about_ca_system_score_gemma":0.02658833,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9749244,"about_ca_topic_score_gemma":0.9911146,"domain_scores_codex":[0.9983073,0.0002223293,0.00006492962,0.00009075831,0.0005067067,0.0008078422],"domain_scores_gemma":[0.9943799,0.0006831434,0.001586043,0.0002044296,0.001338977,0.001807393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001692381,0.00005366782,0.9763788,0.0000736412,0.000149429,0.0004668475,0.004093118,0.001370024,0.000189275,0.002588799,0.005436411,0.009030837],"study_design_scores_gemma":[0.000008326707,0.00001855626,0.9842444,0.00004137396,0.00004170765,0.00004454099,0.00857672,0.0003217087,0.00004045479,0.0002003692,0.006452699,0.00000919848],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9706941,0.0005682755,0.0000744337,0.002501454,0.00001641351,0.00003544633,0.002367948,0.000008042091,0.02373393],"genre_scores_gemma":[0.9934559,0.0005230366,0.00007663123,0.0001587347,0.00001282536,0.00001904238,0.0008315144,0.000005509914,0.004916809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0369696,"threshold_uncertainty_score":0.2682345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05958845482673922,"score_gpt":0.3408692949789497,"score_spread":0.2812808401522104,"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."}}