{"id":"W2950272048","doi":"","title":"Telling Localized Indigenous Histories of Trade through AMS Dating and Bayesian Chronological Modeling in Southern Ontario, Canada","year":2019,"lang":"en","type":"article","venue":"The 84th Annual Meeting of the Society for American Archaeology","topic":"Indigenous Studies and Ecology","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Indigenous; Bayesian probability; Geography; History; Geology; Archaeology; Computer science; Artificial intelligence","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":[0.001223538,0.0002883215,0.0004256463,0.002040269,0.003773704,0.001828685,0.001170003,0.000428067,0.002389747],"category_scores_gemma":[0.004351971,0.0003757344,0.0003038696,0.005186339,0.001085376,0.0007167783,0.0008575835,0.0005893119,0.0002750076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0269567,"about_ca_system_score_gemma":0.03719997,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9984574,"about_ca_topic_score_gemma":0.9995546,"domain_scores_codex":[0.9995259,0.00006723015,0.00003105062,0.0001192035,0.0001181314,0.0001384417],"domain_scores_gemma":[0.9974579,0.0004509543,0.0002830922,0.000111804,0.001510889,0.0001853337],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002636171,0.00005323342,0.8628386,0.0002656136,0.00028499,0.0003448695,0.02448046,0.01439398,0.001952287,0.004896038,0.007735389,0.08249093],"study_design_scores_gemma":[0.00002086431,0.00001552344,0.9455978,0.0002240024,0.0001307529,0.00007499121,0.01475936,0.01629797,0.0004867657,0.001240287,0.0210784,0.00007331946],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9828221,0.001586985,0.002596409,0.0007952105,0.00002348394,0.00003525222,0.003898461,0.00004207751,0.008200058],"genre_scores_gemma":[0.9920884,0.0007314722,0.001868126,0.00006885528,0.000008286705,0.00001812681,0.001064688,0.00003012402,0.004121925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0269567,"threshold_uncertainty_score":0.1955854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02076420228949992,"score_gpt":0.2870506322065168,"score_spread":0.2662864299170169,"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."}}