{"id":"W7081629208","doi":"10.5281/zenodo.17122530","title":"Human in the Loop Adaptive Active Learning Lithium Production","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Lithium (medication); Production (economics); Loop (graph theory); Active learning (machine learning); Lithium carbonate; Code (set theory); Extraction (chemistry); Matching (statistics)","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":[0.0008752479,0.003320286,0.001224652,0.001613341,0.0007900865,0.001419294,0.00450305,0.002295513,0.01989153],"category_scores_gemma":[0.002973969,0.000609372,0.001510813,0.002860232,0.0006865574,0.0008920787,0.001570539,0.002151857,0.03252282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001537944,"about_ca_system_score_gemma":0.001585253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02711111,"about_ca_topic_score_gemma":0.07417963,"domain_scores_codex":[0.999263,0.0001309229,0.0000552039,0.000218995,0.0002306329,0.0001013088],"domain_scores_gemma":[0.998941,0.0003061453,0.00005765657,0.0003190045,0.0002800757,0.00009612823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000220172,0.0001806228,0.001246937,0.0007236719,0.00007466278,0.00007377583,0.00002646988,0.007556943,0.000599694,0.0007508173,0.9748512,0.01369505],"study_design_scores_gemma":[0.001216415,0.0002454153,0.006498759,0.0004613459,0.0001093577,0.0003287892,0.0001579805,0.07121171,0.008978955,0.01124895,0.8994082,0.0001340854],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003978805,0.0006471327,0.003052616,0.0004851313,0.0002683034,0.0001214597,0.9771101,0.01052755,0.00380884],"genre_scores_gemma":[0.004368678,0.0001618811,0.004011531,0.0001296305,0.00001446303,0.0001656378,0.9889819,0.0002781503,0.001888134],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02711111,"threshold_uncertainty_score":0.06654382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0341464342037236,"score_gpt":0.2534013281949907,"score_spread":0.2192548939912671,"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."}}