{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003470992,0.0004317592,0.0007043569,0.0001250604,0.0001228216,0.0002519638,0.001317459,0.000408458,0.3822821],"category_scores_gemma":[0.001293426,0.0003701734,0.00007648016,0.0001573483,0.00007676065,0.0002371329,0.0008996496,0.000326173,0.01051251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004397168,"about_ca_system_score_gemma":0.000116671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004970734,"about_ca_topic_score_gemma":0.00002526739,"domain_scores_codex":[0.9972423,0.000227241,0.0004891008,0.0008284456,0.0007058672,0.0005069893],"domain_scores_gemma":[0.9980375,0.0003246078,0.000241887,0.001114161,0.0001181154,0.0001637624],"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":[0.00001401644,0.00002314795,0.000005830927,0.0008842808,0.000005093388,0.000006564314,0.00001258794,0.00006260239,0.04179173,5.913315e-7,0.9571837,0.000009912869],"study_design_scores_gemma":[0.0002377223,0.0001247356,0.0001420526,0.001694963,0.00002349147,0.00001853038,0.000004386629,0.00009641911,0.00977655,0.000008207556,0.9874487,0.0004242316],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009170421,0.000409915,0.000001216704,0.00004699977,0.0003686258,0.0004634378,0.9975368,0.00004993256,0.000206018],"genre_scores_gemma":[0.0001864836,0.00002193434,0.0002025021,0.0002354669,0.0001740856,0.00006187453,0.9987516,0.00003334341,0.000332743],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3717696,"threshold_uncertainty_score":0.999875,"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."}}