{"id":"W2301216567","doi":"10.48550/arxiv.1603.04521","title":"Optimization of layer composition for ILD ECAL","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Plasma Diagnostics and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Particle Physics","funders":"","keywords":"Composition (language); Layer (electronics); Business; Materials science; Composite material; Philosophy; Linguistics","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":[0.0004395304,0.001246963,0.0005668501,0.0007161,0.0005876804,0.001388759,0.001060603,0.0007791207,0.00372133],"category_scores_gemma":[0.001319691,0.0004735464,0.0004300323,0.000574374,0.0002775717,0.001140329,0.0006901661,0.0006366025,0.0009379574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001259788,"about_ca_system_score_gemma":0.0008691243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001609128,"about_ca_topic_score_gemma":0.002848994,"domain_scores_codex":[0.9997551,0.00003246354,0.00001107251,0.00006143823,0.00007636748,0.00006351568],"domain_scores_gemma":[0.9995924,0.00008431108,0.00008319233,0.00004928033,0.0001523159,0.00003857006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000711643,0.0005179101,0.0068655,0.0006936764,0.0001931295,0.000476088,0.0001227591,0.6227551,0.278188,0.009583102,0.004984853,0.07490826],"study_design_scores_gemma":[0.00008544959,0.0003309429,0.001834662,0.00004204069,0.0001450084,0.000183281,0.0001097577,0.8984897,0.09093274,0.001587658,0.006228699,0.000029999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6265209,0.00301792,0.3171751,0.0008241845,0.0002761179,0.0002470015,0.000741586,0.001961419,0.0492357],"genre_scores_gemma":[0.8992224,0.0003374256,0.09685566,0.0001162917,0.00001367524,0.0001064787,0.0002799809,0.0003586975,0.002709385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00372133,"threshold_uncertainty_score":0.01244909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.049722478265137,"score_gpt":0.1735034333878576,"score_spread":0.1237809551227206,"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."}}