{"id":"W4284706833","doi":"10.1186/s12919-022-00234-x","title":"Precision Public Health Initiatives in Cancer: Proceedings from the Transdisciplinary Conference for Future Leaders in Precision Public Health","year":2022,"lang":"en","type":"article","venue":"BMC Proceedings","topic":"Interdisciplinary Research and Collaboration","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Cancer Institute","keywords":"Public health; Conversation; Population health; Public relations; Field (mathematics); Session (web analytics); International health; Medicine; Medical education; Health policy; Political science; Engineering ethics; Sociology; Computer science; Engineering; Nursing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01439208,0.0003670776,0.0007102943,0.001028419,0.001673658,0.001241983,0.002294925,0.0001317634,0.0003700897],"category_scores_gemma":[0.001834051,0.000262142,0.0001661612,0.005168477,0.0002457713,0.003143071,0.0008235075,0.000982356,0.00001141148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001328673,"about_ca_system_score_gemma":0.004066647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002075714,"about_ca_topic_score_gemma":0.005984657,"domain_scores_codex":[0.9921057,0.0003744923,0.001653148,0.00147765,0.002991661,0.001397409],"domain_scores_gemma":[0.9959427,0.00115627,0.0008354281,0.0002909882,0.00133802,0.000436553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.002533583,0.00125588,0.1922761,0.0002212658,0.00003353776,0.000002745729,0.5897546,0.00009320298,0.0003802442,0.01918802,0.090858,0.1034028],"study_design_scores_gemma":[0.002451676,0.002238278,0.2154782,0.0002030646,0.000002830513,0.000007994226,0.6980291,0.01458478,0.00005115105,0.02773054,0.03878482,0.0004375314],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7653837,0.001523261,0.001207758,0.2265276,0.0005328389,0.003398015,0.0003926121,0.00007129996,0.0009629428],"genre_scores_gemma":[0.9938837,0.0002352717,0.00112564,0.001214683,0.0004577144,0.002560942,0.00007765222,0.00003966611,0.0004047094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2285,"threshold_uncertainty_score":0.9999831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3231959954092243,"score_gpt":0.46133178806144,"score_spread":0.1381357926522156,"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."}}