{"id":"W2797370263","doi":"10.1039/c8cp01715h","title":"Eliminating common biases in modelling the electrical conductivity of carbon nanotube–polymer nanocomposites","year":2018,"lang":"en","type":"article","venue":"Physical Chemistry Chemical Physics","topic":"Carbon Nanotubes in Composites","field":"Materials Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Toronto Public Health","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Carbon nanotube; Nanocomposite; Materials science; Conductivity; Polymer; Polymer nanocomposite; Electrical resistivity and conductivity; Nanotube; Nanotechnology; Composite material; Chemistry; Physical chemistry; Electrical engineering; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008088228,0.0005241313,0.0003959706,0.0004338554,0.0003809498,0.0007817146,0.0007051263,0.001405895,0.0008141421],"category_scores_gemma":[0.004318594,0.0003808509,0.0004438015,0.000448272,0.0005515295,0.001215845,0.0006516218,0.0007833301,0.0001804496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006415916,"about_ca_system_score_gemma":0.0009590042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007624118,"about_ca_topic_score_gemma":0.006330427,"domain_scores_codex":[0.9996679,0.00009903503,0.00002271545,0.00003756677,0.0001079976,0.00006485363],"domain_scores_gemma":[0.9990939,0.0005243763,0.00008728356,0.0000976093,0.0001574303,0.00003936715],"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":[0.0000261088,0.00002081962,0.001137837,0.00002751554,0.000009258416,0.00007310419,0.00004270014,0.9894962,0.003337839,0.003359126,0.00006265396,0.002406926],"study_design_scores_gemma":[0.000003889669,0.00001267006,0.0001285613,0.000007628402,0.000004447219,0.000009757537,0.00001320279,0.995924,0.002347125,0.001254567,0.0002891238,0.000005089503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8183321,0.0006342944,0.1639327,0.0005117712,0.0001315672,0.0001126765,0.0002932603,0.0002941306,0.01575748],"genre_scores_gemma":[0.9837099,0.0002452215,0.0137234,0.00005196383,0.0000119356,0.000088122,0.00007846692,0.00007028058,0.002020591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007624118,"threshold_uncertainty_score":0.01515949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0259210039189142,"score_gpt":0.2720798839561219,"score_spread":0.2461588800372077,"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."}}