{"id":"W6927370088","doi":"10.3389/fcomm.2024.1512014.s001","title":"Data Sheet 1_A content analysis of government-issued social media posts during multi-jurisdictional enteric illness outbreaks in Canada.docx","year":2024,"lang":"en","type":"dataset","venue":"Figshare","topic":"Botanical Research and Applications","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social media; CLARITY; Public health; Health communication; Content analysis; Agency (philosophy); Proxy (statistics); Microblogging","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008419005,0.0002109352,0.0004321036,0.00002722817,0.000117161,0.00006646672,0.001237634,0.0001604302,0.0951667],"category_scores_gemma":[0.0006420796,0.00009889429,0.0001343028,0.0009761871,0.00001677654,0.00007557089,0.0009764942,0.0003922872,0.0003019624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003959833,"about_ca_system_score_gemma":0.000168615,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1882199,"about_ca_topic_score_gemma":0.9822772,"domain_scores_codex":[0.9976249,0.00006602855,0.0004372534,0.0005753707,0.0009392974,0.0003571257],"domain_scores_gemma":[0.998921,0.0004441055,0.0001769966,0.0002129141,0.00007378289,0.0001711449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001776337,0.00008452289,0.0000806978,0.00007041347,0.000208318,0.0000284778,0.000006874514,0.000001360762,0.0004135154,2.951741e-7,0.9977859,0.00130182],"study_design_scores_gemma":[0.0001118029,0.00001133507,0.2610856,0.0002591585,0.0001421932,9.956871e-7,0.0001830308,0.0001679963,0.00003680811,0.000001474491,0.7377712,0.0002284684],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.008439267,0.0001862168,1.196929e-8,0.0003940829,0.00005238591,0.0002125856,0.9906954,0.00001051527,0.000009520672],"genre_scores_gemma":[0.06154071,0.00004220854,0.000001614521,0.00004749048,0.0001906883,0.0001404742,0.9380004,0.000001566804,0.00003487304],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7940572,"threshold_uncertainty_score":0.9056605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1140830162387403,"score_gpt":0.2997079330898217,"score_spread":0.1856249168510814,"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."}}