{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00106715,0.0001763305,0.0006824127,0.00001666568,0.001171354,8.666302e-7,0.0004053611,0.0001259627,0.00002645428],"category_scores_gemma":[0.0001861348,0.0001115967,0.0002008423,0.0001606109,0.001137689,0.0000241309,0.0004968225,0.0006304397,4.274439e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006160807,"about_ca_system_score_gemma":0.001261356,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9659441,"about_ca_topic_score_gemma":0.9793423,"domain_scores_codex":[0.9976625,0.0003346426,0.0006553543,0.0002660474,0.0001449869,0.0009364823],"domain_scores_gemma":[0.9978291,0.001127241,0.000637171,0.0002781487,0.00009308627,0.00003529438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001432287,0.00002327043,0.1418201,0.0001788654,0.0001348632,3.315678e-7,0.8523551,0.004463053,0.00007333324,0.0003415408,0.00008287832,0.0003834259],"study_design_scores_gemma":[0.0009551346,0.0005251719,0.002131818,0.00009801217,0.00007594994,0.000003937711,0.9848821,0.007061061,0.00001999151,0.001439169,0.00259633,0.0002113471],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946731,0.0003510198,0.0006042594,0.002544512,0.0003523933,0.00113198,0.00006736068,0.00001376491,0.000261642],"genre_scores_gemma":[0.995116,0.00004701238,0.00350319,0.0009692446,0.00006143442,0.00005973172,0.000005952145,0.0000225971,0.0002148415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1396883,"threshold_uncertainty_score":0.9009224,"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."}}