{"id":"W2964741033","doi":"10.29173/cais1049","title":"Performance of Crowdsourcing to Monitor and Filter Relevant S c ien tific L iterature","year":2018,"lang":"en","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Crowdsourcing; Filter (signal processing); Data science; Measure (data warehouse); Computer science; Psychology; Data mining; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01727218,0.001649895,0.00171888,0.005162757,0.001946444,0.003253773,0.00161507,0.002470836,0.001667491],"category_scores_gemma":[0.04644974,0.00040425,0.0008784592,0.002592404,0.000972493,0.002225806,0.003062606,0.001131931,0.002033014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0014281,"about_ca_system_score_gemma":0.003237937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02320783,"about_ca_topic_score_gemma":0.01317971,"domain_scores_codex":[0.988169,0.004910552,0.0006377837,0.002446915,0.003149397,0.0006864133],"domain_scores_gemma":[0.9503742,0.03106602,0.003577858,0.00491857,0.007230388,0.002832983],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01039463,0.003679605,0.2493772,0.002207306,0.002038658,0.0008424407,0.006828691,0.09092952,0.03478457,0.003678427,0.03582406,0.559415],"study_design_scores_gemma":[0.0008318844,0.004018204,0.1017075,0.0003269247,0.00050912,0.0004613264,0.005333278,0.8123223,0.02631332,0.0114352,0.03628382,0.0004571345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.920058,0.002897217,0.04209899,0.003503009,0.000968584,0.001490752,0.003923121,0.004818545,0.02024183],"genre_scores_gemma":[0.9642691,0.0002938097,0.02842705,0.0005786908,0.0001922228,0.0003790741,0.002171153,0.000135489,0.003553472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9827278,"threshold_uncertainty_score":0.09134513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01551869438075382,"score_gpt":0.2514552896136279,"score_spread":0.2359365952328741,"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."}}