{"id":"W4398701851","doi":"10.7910/dvn/oe6umr/dw1pvm","title":"Becker Centola Porter - Wisdom of Partisan Crowds - Supplementary Dataset.csv","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Crowds; Information retrieval; Computer science; Artificial intelligence; Computer security","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":[0.0007360701,0.0006801938,0.0009051096,0.0003636366,0.0001758729,0.000365044,0.003387515,0.0003625847,0.02299689],"category_scores_gemma":[0.00007839237,0.0006780745,0.00024336,0.0003743388,0.0002370442,0.000851221,0.002552993,0.0006623009,0.02322431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001026476,"about_ca_system_score_gemma":0.0003169982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001870841,"about_ca_topic_score_gemma":0.0003051423,"domain_scores_codex":[0.9954833,0.0002212735,0.001023119,0.00139592,0.0009973922,0.0008789995],"domain_scores_gemma":[0.9923003,0.0001522531,0.0007260313,0.006401673,0.0001106793,0.0003090813],"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":[0.00002640623,0.0001847153,0.00005512444,0.000288228,0.000163349,0.0002460065,0.00007203478,0.00002526603,0.0001153623,0.00005623106,0.9982049,0.0005623825],"study_design_scores_gemma":[0.0007968691,0.00009739984,0.00004299275,0.0002395686,0.0002006314,0.00008513031,0.00006551768,0.0005102561,0.0005195745,0.00001688696,0.9967253,0.0006998924],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001209534,0.000005821084,0.001639706,0.00002957116,0.001820612,0.0004817208,0.9957916,0.00005427904,0.00005574066],"genre_scores_gemma":[0.0002014201,0.00009238865,0.002875468,0.0009066283,0.000266176,0.0000179992,0.9953817,0.00003939743,0.0002187844],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.005005753,"threshold_uncertainty_score":0.999567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0188367639231898,"score_gpt":0.2539024167427774,"score_spread":0.2350656528195876,"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."}}