{"id":"W2062728651","doi":"10.1021/ie0613265","title":"Mixing Time Analysis Using Colorimetric Methods and Image Processing","year":2007,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Fluid Dynamics and Mixing","field":"Engineering","cited_by":146,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Repeatability; Mixing (physics); RGB color model; Computer science; Micromixing; Biological system; Artificial intelligence; Chemistry; Analytical Chemistry (journal); Chromatography","routes":{"ca_aff":true,"ca_fund":true,"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"],"consensus_categories":[],"category_scores_codex":[0.003886352,0.0002558634,0.0003932842,0.000795515,0.0001687981,0.0001739032,0.0002599717,0.0003660179,0.00005813976],"category_scores_gemma":[0.0006517625,0.0002946409,0.00009897958,0.004146248,0.00007693692,0.0001825791,0.0001460171,0.001137293,0.000004875194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003969932,"about_ca_system_score_gemma":0.00007004791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003899854,"about_ca_topic_score_gemma":6.050205e-7,"domain_scores_codex":[0.9978095,0.00003511752,0.0004209355,0.0003740559,0.0004491807,0.0009112193],"domain_scores_gemma":[0.998724,0.0005058962,0.00003247316,0.000275679,0.0001670797,0.0002949021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009269126,0.000009587106,0.000118839,0.0001129673,0.0001794921,0.00002876747,0.00006047071,0.06127678,0.930339,0.000004251696,0.00002856407,0.007831983],"study_design_scores_gemma":[0.0002339048,0.000006850982,0.0000775964,0.00004661251,0.00007660149,0.00001357932,0.00003867883,0.6413531,0.3575213,0.000004487243,0.0004100516,0.0002172445],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.962012,0.0009848961,0.03413646,0.000008163152,0.0001313608,0.0001835882,0.000008967609,0.0003262975,0.002208255],"genre_scores_gemma":[0.9814833,0.00002203435,0.01748675,0.00000146032,0.0006511794,0.000008485478,0.00001763867,0.0000942173,0.0002348967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5800763,"threshold_uncertainty_score":0.9999506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06348545705246042,"score_gpt":0.3835289873337978,"score_spread":0.3200435302813374,"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."}}