{"id":"W6958455918","doi":"10.6084/m9.figshare.22259500","title":"Supplementary Figure 1","year":2023,"lang":"en","type":"other","venue":"Figshare","topic":"Legal and Regulatory Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multilocus sequence typing; Sequence (biology); Colored; Timeline; Line (geometry); White (mutation)","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002209786,0.001737774,0.001353557,0.003484265,0.002378257,0.005791251,0.002934335,0.002987938,0.9017946],"category_scores_gemma":[0.01324703,0.001133108,0.001474156,0.003965601,0.0005347364,0.003000566,0.0019652,0.001973658,0.5834897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00212777,"about_ca_system_score_gemma":0.004585067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0090544,"about_ca_topic_score_gemma":0.01527676,"domain_scores_codex":[0.9981558,0.000250092,0.0001516286,0.0004284047,0.0007726257,0.0002415296],"domain_scores_gemma":[0.9931258,0.002280809,0.0003625597,0.0009789215,0.002557553,0.0006943347],"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":[0.00008533665,0.00002436868,0.0002766659,0.0005809099,0.00001441224,0.00006362412,0.000026509,0.0001728164,0.0002714539,0.001838374,0.9836026,0.0130428],"study_design_scores_gemma":[0.0001017213,0.00002828196,0.001157818,0.0003265665,0.00001555481,0.0001043128,0.00005509895,0.0002170944,0.0002712013,0.002725165,0.9949749,0.00002231651],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0005359807,0.0005550919,0.004442798,0.00187746,0.004447005,0.0004411303,0.9063271,0.006719958,0.07465348],"genre_scores_gemma":[0.00616141,0.001249198,0.01211398,0.003988585,0.001166021,0.001132153,0.8237099,0.005583056,0.1448957],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.09820539,"threshold_uncertainty_score":0.1400781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03714489326028669,"score_gpt":0.3075945385222269,"score_spread":0.2704496452619402,"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."}}