{"id":"W7006060729","doi":"","title":"Substance Use in Saskatchewan: Calibration and Parameterization for a Computational Epidemiology Approach to Substance Use Research","year":2025,"lang":"en","type":"article","venue":"University Library (University of Saskatchewan)","topic":"Census and Population Estimation","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Health Canada; Public Health Agency of Canada; Government of Canada; U.S. Department of Justice; Public Health Agency; University of Saskatchewan","keywords":"Harm reduction; Harm; Public health; Epidemiology; Drug overdose; Informatics; Substance abuse; Poison control; Health care","routes":{"ca_aff":false,"ca_fund":true,"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":[0.007963415,0.0008083724,0.0008588209,0.001840495,0.001316795,0.002445569,0.003150536,0.00139195,0.009158733],"category_scores_gemma":[0.03297676,0.0009957037,0.002255626,0.003556399,0.001277555,0.001311546,0.002742298,0.002554659,0.001279966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005990481,"about_ca_system_score_gemma":0.009526588,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5181626,"about_ca_topic_score_gemma":0.4411632,"domain_scores_codex":[0.9963949,0.002367754,0.0001432619,0.0006303194,0.000204939,0.0002588961],"domain_scores_gemma":[0.9780495,0.01620919,0.0008706084,0.001893905,0.002650561,0.0003262152],"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":[0.0004054914,0.000317522,0.1679225,0.0002138365,0.0007021726,0.000365586,0.0005299,0.7334472,0.0004100948,0.03646697,0.01520199,0.04401674],"study_design_scores_gemma":[0.0001067137,0.00007052446,0.02902075,0.0001191749,0.00009880804,0.00009722523,0.0007028627,0.9415283,0.0002460546,0.01961328,0.008338785,0.00005747372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5857516,0.0008134848,0.3575282,0.005744708,0.0003243036,0.001392154,0.03151207,0.002491888,0.01444167],"genre_scores_gemma":[0.8054425,0.0005898935,0.1662433,0.0008552716,0.00005594021,0.001897471,0.01823682,0.0002643203,0.006414538],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4818374,"threshold_uncertainty_score":0.96935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1125237968859095,"score_gpt":0.2968698947068762,"score_spread":0.1843460978209667,"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."}}