{"id":"W2470312821","doi":"10.2533/chimia.2015.698","title":"12. Freiburger Symposium 2015: Smart Solutions in the Chemical Process & Product Development – Case Studies from the Chemical Industry","year":2015,"lang":"en","type":"article","venue":"CHIMIA International Journal for Chemistry","topic":"Chemistry and Chemical Engineering","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Product (mathematics); Process (computing); Process development; Process engineering; Computer science; Environmental science; Biochemical engineering; Chemistry; Engineering; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0005550298,0.000240545,0.0001785785,0.000009925404,0.0001671513,0.00008295402,0.0009441082,0.0001795554,0.0001991402],"category_scores_gemma":[0.0007501353,0.000157428,0.000108776,0.00009394134,0.0002185069,0.0001695891,0.0002829977,0.00104387,0.00001600923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006238699,"about_ca_system_score_gemma":0.0001067508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001802103,"about_ca_topic_score_gemma":0.00001080968,"domain_scores_codex":[0.9981813,0.0000149055,0.000456301,0.0003396698,0.0006450292,0.0003627343],"domain_scores_gemma":[0.9991172,0.0002093344,0.0001450805,0.0002439624,0.000103409,0.0001810272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001702846,0.000450384,0.006820601,0.00005402095,0.0003456367,0.0003404177,0.007588007,0.002975027,0.9163088,0.00000590421,0.06417705,0.0007639274],"study_design_scores_gemma":[0.00127538,0.000005324154,0.0001568464,0.0001580699,0.00005972325,0.006510446,0.002545838,0.0004890824,0.9337028,0.001058009,0.05353586,0.0005026839],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934118,0.0001993546,0.00008153899,0.004361526,0.0004115711,0.0001392866,0.0000321139,0.00002441615,0.001338405],"genre_scores_gemma":[0.9971959,0.00001315801,0.0007347031,0.0002544929,0.001145503,0.000111487,0.00009099364,0.00001870568,0.0004350616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.017394,"threshold_uncertainty_score":0.6419725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05125175274333017,"score_gpt":0.3090869036531016,"score_spread":0.2578351509097714,"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."}}