{"id":"W2616417825","doi":"10.2196/resprot.5851","title":"Crowdsourced Identification of Possible Allergy-Associated Factors: Automated Hypothesis Generation and Validation Using Crowdsourcing Services","year":2017,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Crowdsourcing; Identification (biology); Data science; Computer science; World Wide Web; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002821511,0.0002236928,0.0003074908,0.00039484,0.002032508,0.002923719,0.001198655,0.0002068834,0.000007252489],"category_scores_gemma":[0.0005373051,0.0002145382,0.00007571085,0.0003915063,0.0002202402,0.001578899,0.0005654757,0.0002822872,0.000007345295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001437713,"about_ca_system_score_gemma":0.0001417467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003433805,"about_ca_topic_score_gemma":0.00004166407,"domain_scores_codex":[0.9965751,0.0006174273,0.000629226,0.0006641535,0.0009619611,0.0005520967],"domain_scores_gemma":[0.9965953,0.0002212637,0.0007912009,0.001438048,0.0007984066,0.0001558308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002582345,0.0002164582,0.008410595,0.0003188723,0.00005329582,0.00000524539,0.002794378,0.0009818508,0.9807721,0.0004533286,0.0001549043,0.005813104],"study_design_scores_gemma":[0.0006936379,0.0001420853,0.05626989,0.001005074,0.000003409422,0.000004869939,0.0001488589,0.5202964,0.4205935,0.0003719313,0.0002232913,0.0002470154],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9277726,0.000005895043,0.007539189,0.0001484551,0.00002606949,0.06394896,0.000007386408,0.0003805098,0.0001709055],"genre_scores_gemma":[0.9537517,9.963039e-7,0.002772525,0.000007616946,0.00008213057,0.04319461,0.00001039638,0.000032475,0.00014754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5601786,"threshold_uncertainty_score":0.9992667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2389973154109489,"score_gpt":0.4639238991815707,"score_spread":0.2249265837706218,"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."}}