{"id":"W4414729867","doi":"10.1039/d5dd00285k","title":"Human-AI synergy in adaptive active learning for continuous lithium carbonate crystallization optimization","year":2025,"lang":"en","type":"article","venue":"Digital Discovery","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Natural Resources Canada","funders":"Natural Resources Canada","keywords":"Active learning (machine learning); Process (computing); Crystallization; Lithium carbonate; Lithium (medication)","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":[],"consensus_categories":[],"category_scores_codex":[0.00004220099,0.0001351116,0.0001920622,0.0001669705,0.00005303812,0.0002616016,0.00006064342,0.00008426342,0.000002288159],"category_scores_gemma":[0.00006012655,0.0001455783,0.00006933193,0.0002317471,0.0000163565,0.0008993665,0.00001572148,0.00011353,0.00000153067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001511223,"about_ca_system_score_gemma":0.00002108169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003584852,"about_ca_topic_score_gemma":0.00004585893,"domain_scores_codex":[0.9993211,0.00001725736,0.000230017,0.0001731543,0.00008152248,0.0001769566],"domain_scores_gemma":[0.9997647,0.00004617256,0.00003695302,0.00007956156,0.00005045738,0.00002213923],"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":[0.00005134044,0.00001986851,0.0001465153,0.00004091573,0.00004981906,0.000001583222,0.0001208806,0.993398,0.00142506,0.002588175,0.0000964396,0.00206139],"study_design_scores_gemma":[0.001015148,0.00007269089,0.0006515054,0.0001409589,0.00001194205,8.203064e-7,0.000817386,0.9925163,0.0009467558,0.0004372079,0.003174452,0.0002148161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08836894,0.0002291956,0.8059959,0.00004765528,0.0007392554,0.0007987522,0.0000727006,0.0004613839,0.1032862],"genre_scores_gemma":[0.9949021,0.000005270937,0.00001868862,0.000032776,0.00004001976,0.0001281833,0.00009563052,0.00002512454,0.004752195],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9065332,"threshold_uncertainty_score":0.5936512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005180648332765751,"score_gpt":0.2176469434029034,"score_spread":0.2124662950701377,"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."}}