{"id":"W4399349135","doi":"10.1088/1742-6596/2771/1/012008","title":"Thermal-electric modelling and multi-state joint parameter identification of lithium-ion batteries","year":2024,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Energy","funders":"","keywords":"Lithium (medication); Joint (building); Identification (biology); Ion; Thermal; State (computer science); Materials science; Computer science; Chemistry; Engineering; Physics; Thermodynamics; Structural engineering; Algorithm; Psychology","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":[0.0001528101,0.00012157,0.0002215076,0.0001553234,0.00002907107,0.0001228199,0.0001374669,0.00004574523,0.000006140931],"category_scores_gemma":[0.00003157212,0.0001031114,0.00005311219,0.0002152919,0.0001106836,0.0008863568,0.00004035451,0.0003387957,0.00000291314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003693018,"about_ca_system_score_gemma":0.00003364697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000172459,"about_ca_topic_score_gemma":5.354976e-7,"domain_scores_codex":[0.9991272,0.00002085159,0.0004098042,0.0001007582,0.0001813594,0.000160007],"domain_scores_gemma":[0.9994686,0.00006486893,0.0001087996,0.0001351212,0.0001931098,0.00002948082],"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":[0.00001573351,0.00001257779,0.00004236191,0.0002926041,0.00006499531,0.000009005267,0.000884997,0.08936378,0.767177,0.0008678135,0.00001419973,0.1412549],"study_design_scores_gemma":[0.00006389999,0.00007327738,0.0002332357,0.000131154,0.000009896481,0.00001249823,0.0001674132,0.2090995,0.77906,0.01099739,0.0000635793,0.00008818808],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4860841,0.0005733536,0.5130164,0.0000839884,0.0001133084,0.00004808271,0.000005695281,0.0000530607,0.00002201163],"genre_scores_gemma":[0.9934522,0.001632479,0.004778877,0.000002931003,0.00003882084,0.000004279239,0.000001114723,0.00002160322,0.00006772012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5082375,"threshold_uncertainty_score":0.4204759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0492112141986841,"score_gpt":0.2774071573375408,"score_spread":0.2281959431388567,"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."}}