{"id":"W4394056848","doi":"10.5281/zenodo.3463386","title":"Buffer screen by DSLS of C-HEAT and N-HEAT domains","year":2019,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","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","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00182321,0.0003146624,0.0004689339,0.0002629697,0.00112166,0.001051632,0.002272146,0.0002105364,0.03897896],"category_scores_gemma":[0.0009597082,0.0002996413,0.00005404239,0.0003481999,0.0005796623,0.0002975092,0.002806916,0.0004001762,0.013229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000101201,"about_ca_system_score_gemma":0.000009458582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003327886,"about_ca_topic_score_gemma":8.679142e-7,"domain_scores_codex":[0.9964891,0.0008075002,0.0004945652,0.0008477485,0.0008259185,0.0005351342],"domain_scores_gemma":[0.9980335,0.0000786284,0.0002056383,0.001094896,0.0003703529,0.0002169425],"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":[0.00004559142,0.00005840465,9.451215e-7,0.0002473636,0.0000102061,0.000004422396,0.00007754216,0.00008085088,0.1585902,0.00005006728,0.840535,0.0002994759],"study_design_scores_gemma":[0.0003715014,0.0003208155,0.00006246437,0.0001054618,0.00003023195,0.00007305302,0.00003116372,0.0001529624,0.003891736,0.00002755982,0.9946283,0.0003047257],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01114478,0.0001547765,0.0004542223,0.0002344851,0.0003625675,0.0006209952,0.9832676,0.0002550437,0.003505549],"genre_scores_gemma":[0.004452941,0.000277802,0.0004394604,0.0001841648,0.0001820701,7.44894e-8,0.9928375,0.0009596134,0.000666399],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1546984,"threshold_uncertainty_score":0.9999854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02102625788969461,"score_gpt":0.2596221499378607,"score_spread":0.2385958920481661,"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."}}