{"id":"W2509465192","doi":"","title":"Towards a Common Format for Computational Materials Science Data","year":2016,"lang":"en","type":"preprint","venue":"MPG.PuRe (Max Planck Society)","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Banting and Best Diabetes Centre, University of Toronto; European Commission","keywords":"Standardization; Computer science; Metadata; Code (set theory); File format; Data exchange; Software; Data science; Data structure; Software engineering; Information retrieval; World Wide Web; Programming language","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.04005376,0.002276703,0.002789214,0.01314387,0.003554356,0.02152786,0.0126963,0.005765522,0.009930744],"category_scores_gemma":[0.1005796,0.00219324,0.004301307,0.01623138,0.003837282,0.01998282,0.01743949,0.01603908,0.01533285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00403211,"about_ca_system_score_gemma":0.0127706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003161534,"about_ca_topic_score_gemma":0.002022307,"domain_scores_codex":[0.9744228,0.008038972,0.006932403,0.002606035,0.007106455,0.0008932736],"domain_scores_gemma":[0.895779,0.02090042,0.003593623,0.05155821,0.02442457,0.003744218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000360922,0.0003187221,0.002542289,0.001511793,0.0002195241,0.0004943646,0.001398297,0.01367837,0.004530564,0.6761262,0.157009,0.14181],"study_design_scores_gemma":[0.0002047202,0.0001008155,0.0009326404,0.001479602,0.0001153409,0.000434639,0.0007896005,0.03686984,0.01355605,0.3550838,0.5901253,0.0003077296],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001822604,0.0005452542,0.9633575,0.003596682,0.00110281,0.0005146323,0.01213495,0.01120801,0.005717564],"genre_scores_gemma":[0.01711514,0.001374624,0.9116977,0.001794508,0.000625948,0.001957886,0.05505835,0.007408643,0.002967177],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9873037,"threshold_uncertainty_score":0.211827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04395569801139747,"score_gpt":0.3356261776042354,"score_spread":0.2916704795928379,"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."}}