{"id":"W6888490768","doi":"10.21227/rd1e-6k71","title":"RSSdata_HumanHuman","year":2020,"lang":"en","type":"dataset","venue":"IEEE DataPort","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Guelph","funders":"","keywords":"RSS; Identification (biology); Data collection; Table (database); Set (abstract data type)","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004851526,0.0009637238,0.001069433,0.0003898889,0.0002199628,0.0003026465,0.003891831,0.0005440208,0.00725545],"category_scores_gemma":[0.000219874,0.001015296,0.0002253513,0.0004936309,0.0002721652,0.0006134502,0.0007982006,0.001627567,0.5035365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001948371,"about_ca_system_score_gemma":0.0004228745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006224519,"about_ca_topic_score_gemma":0.0007139461,"domain_scores_codex":[0.9950389,0.000119432,0.0009496985,0.001742845,0.001319699,0.0008294302],"domain_scores_gemma":[0.9934682,0.00005715824,0.0007375538,0.005089533,0.0001005449,0.0005470401],"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":[0.00003740269,0.0001311295,0.000003240438,0.0002333429,0.000210611,0.001413146,0.000009401018,9.667733e-7,0.00009626125,0.000008377917,0.9978111,0.00004499716],"study_design_scores_gemma":[0.0003876112,0.0000747282,0.00002771907,0.00008403284,0.0004373202,0.0000794622,0.000006937611,0.000002369705,0.0000646748,0.0000281838,0.9977374,0.001069567],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000004653819,0.00005713282,0.000005376146,0.00003422215,0.002170239,0.0005757254,0.9961629,0.0004833369,0.0005064316],"genre_scores_gemma":[0.000003943422,0.00008083856,0.00007475272,0.001255845,0.00292934,0.000085849,0.9949064,0.0002701326,0.0003929045],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.496281,"threshold_uncertainty_score":0.9992297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0649428489772638,"score_gpt":0.3175334129313685,"score_spread":0.2525905639541047,"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."}}