{"id":"W2514437433","doi":"10.1021/acs.chemmater.6b02726","title":"Methods and Protocols for Electrochemical Energy Storage Materials Research","year":2016,"lang":"en","type":"article","venue":"Chemistry of Materials","topic":"Advancements in Battery Materials","field":"Engineering","cited_by":175,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Waterloo Institute for Nanotechnology, University of Waterloo; BASF; Natural Resources Canada; U.S. Department of Energy","keywords":"Battery (electricity); Characterization (materials science); Dielectric spectroscopy; Electrochemical energy storage; Electrochemistry; Materials science; Electrochemical cell; X-ray photoelectron spectroscopy; Energy storage; Electrode; Computer science; Nanotechnology; Fabrication; Analytical Chemistry (journal); Chemical engineering; Chemistry; Supercapacitor; Physical chemistry; Engineering; Physics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001404799,0.0002193027,0.0005022457,0.00003886137,0.00004636533,0.00005431831,0.000275626,0.0001815554,0.001079423],"category_scores_gemma":[0.0002980445,0.0001719407,0.00002105606,0.0000537073,0.0001844894,0.000118946,0.000119915,0.00003183011,0.000004118608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008204642,"about_ca_system_score_gemma":0.00002090064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001630503,"about_ca_topic_score_gemma":3.980091e-8,"domain_scores_codex":[0.9983538,0.0001165378,0.0005673256,0.000312117,0.0001807269,0.0004695279],"domain_scores_gemma":[0.9989851,0.0003308362,0.00009792911,0.000392845,0.0001178035,0.00007555621],"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":[0.0001477691,0.00001731658,9.43259e-7,0.001130874,0.00002989039,7.318272e-7,0.00001518766,6.924681e-7,0.9962779,0.0001526394,0.001075388,0.001150658],"study_design_scores_gemma":[0.0006120512,0.00003998095,0.000004737145,0.0002116011,0.000007705124,0.00000827039,0.000006366254,7.528788e-7,0.9833699,0.004171856,0.01136464,0.0002020883],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9740661,0.00007032615,0.02219183,0.00006882865,0.0002659387,0.002555322,0.0003368597,0.0001551225,0.0002896087],"genre_scores_gemma":[0.9481265,0.00007305203,0.03498697,0.00002168742,0.0007250805,0.01495653,0.00005235371,0.0001451117,0.000912701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02593965,"threshold_uncertainty_score":0.9998337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04451646530710045,"score_gpt":0.3900446861153404,"score_spread":0.34552822080824,"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."}}