{"id":"W4393563701","doi":"10.5281/zenodo.3973285","title":"Dagstuhl-15512-ArgQuality","year":2017,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001936456,0.001788121,0.001053936,0.004829754,0.00132183,0.00263626,0.002227414,0.002348702,0.0443615],"category_scores_gemma":[0.008052205,0.0005629489,0.0008054333,0.005231444,0.0008370609,0.002014026,0.003014033,0.001685482,0.04943506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002134842,"about_ca_system_score_gemma":0.002166253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01276967,"about_ca_topic_score_gemma":0.02155714,"domain_scores_codex":[0.9971558,0.0009586512,0.0003689114,0.0005761904,0.0007093691,0.0002310521],"domain_scores_gemma":[0.9967878,0.001267878,0.0003651143,0.0005993736,0.0006788817,0.0003009185],"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":[0.0002728345,0.0001314,0.001677831,0.001675051,0.00003221182,0.0002014791,0.0003320438,0.0004655693,0.0006734466,0.00323939,0.9796532,0.01164555],"study_design_scores_gemma":[0.0002889828,0.00003761625,0.009541434,0.0003071594,0.00001967145,0.0003369699,0.0004382558,0.00103933,0.001153725,0.002412719,0.9843779,0.00004614354],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005497399,0.0006288599,0.001414807,0.0004039138,0.0001499451,0.0001257385,0.9816886,0.00153752,0.008553127],"genre_scores_gemma":[0.004568539,0.0001059227,0.001770189,0.00007563668,0.00001891137,0.0002940185,0.9899783,0.0002400345,0.002948394],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0443615,"threshold_uncertainty_score":0.148404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08256710128103473,"score_gpt":0.3479013900581899,"score_spread":0.2653342887771552,"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."}}