{"id":"W4393504763","doi":"10.5281/zenodo.3463387","title":"Buffer screen by DSLS of C-HEAT and N-HEAT domains","year":2019,"lang":"en","type":"dataset","venue":"Figshare","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Structural Genomics Consortium","funders":"","keywords":"Buffer (optical fiber); Computer science; Telecommunications","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":[0.001583117,0.004368789,0.002545484,0.0029374,0.001688059,0.003035723,0.004502182,0.003682385,0.04911122],"category_scores_gemma":[0.005215186,0.0009431046,0.002566837,0.003893352,0.0006137909,0.001682034,0.001903758,0.00304187,0.06899102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001830093,"about_ca_system_score_gemma":0.002685204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01345447,"about_ca_topic_score_gemma":0.02990521,"domain_scores_codex":[0.9983541,0.0002219688,0.0001397416,0.0006519746,0.0004096164,0.0002226094],"domain_scores_gemma":[0.998165,0.0007014793,0.0001256081,0.000553577,0.0003130675,0.0001412678],"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":[0.0004351422,0.0001112764,0.00190188,0.002327355,0.0001690333,0.00007552359,0.00003528856,0.001405645,0.00302758,0.001349149,0.9844059,0.004756116],"study_design_scores_gemma":[0.001063297,0.0001262009,0.006639855,0.0004966602,0.000262139,0.0001741154,0.0001141197,0.004108822,0.01067982,0.00597021,0.970258,0.0001067154],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000898462,0.0003327802,0.0002998408,0.0001150467,0.00005703132,0.00002144966,0.9943321,0.002568954,0.001374285],"genre_scores_gemma":[0.001195522,0.0001011391,0.0008070562,0.0000834581,0.000005391978,0.00008528957,0.9968587,0.0002636006,0.0005999475],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04911122,"threshold_uncertainty_score":0.1642934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02075003008359183,"score_gpt":0.2782788794203753,"score_spread":0.2575288493367835,"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."}}