{"id":"W2305502609","doi":"10.1149/ma2016-03/2/678","title":"Modeling of Conductivity of Lithium Salt in Electrolytes for Lithium-Ion Batteries","year":2016,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Electrolyte; Conductivity; Lithium (medication); Ionic conductivity; Ternary operation; Ion; Solvation; Battery (electricity); Lithium-ion battery; Materials science; Chemistry; Thermodynamics; Computer science; Organic chemistry; Physical chemistry; Physics; Electrode; Power (physics)","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":[0.0003451064,0.0009266567,0.0009046627,0.0004533719,0.0005248705,0.001033995,0.001503431,0.002046134,0.001569692],"category_scores_gemma":[0.000996827,0.0003106846,0.0007789031,0.0007411467,0.0005403602,0.001472104,0.0004709887,0.0008482913,0.0008669143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001243819,"about_ca_system_score_gemma":0.0007490409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002375024,"about_ca_topic_score_gemma":0.001808841,"domain_scores_codex":[0.9997568,0.00005888863,0.0000173727,0.00004320468,0.0001055772,0.00001817489],"domain_scores_gemma":[0.9998105,0.00009206776,0.00001986989,0.00001345829,0.00005754754,0.000006509784],"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":[0.0001089785,0.0001071576,0.001216398,0.0006975492,0.00003497208,0.0003336165,0.0002150498,0.8826949,0.07257396,0.0227666,0.001600168,0.01765071],"study_design_scores_gemma":[0.000009814325,0.00005868665,0.0001642014,0.00004278275,0.00001216363,0.00006534769,0.0000192931,0.9794698,0.01167681,0.003018721,0.005444187,0.00001815067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.16168,0.01924881,0.7605359,0.001487689,0.0007180652,0.0005795656,0.002335456,0.001286873,0.05212776],"genre_scores_gemma":[0.8968029,0.01180731,0.06553495,0.0002467211,0.0001137112,0.0008574171,0.00102325,0.0002358551,0.02337782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002375024,"threshold_uncertainty_score":0.00902456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02549424321017755,"score_gpt":0.2722845312908903,"score_spread":0.2467902880807128,"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."}}