{"id":"W4251148210","doi":"10.1002/masy.200990026","title":"Macromol. Symp. 285","year":2009,"lang":"en","type":"article","venue":"Macromolecular Symposia","topic":"Graphene and Nanomaterials Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Materials science; Polymer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006467998,0.0001928498,0.0001750679,0.00008402956,0.00007728981,0.00007514875,0.0002278999,0.00009165789,0.00009350565],"category_scores_gemma":[0.000002797779,0.0002050832,0.00009612404,0.0002654844,0.00002043098,0.00006625572,0.00001647297,0.00007564983,0.0003021992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002871005,"about_ca_system_score_gemma":0.000008645301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004960951,"about_ca_topic_score_gemma":0.000001324716,"domain_scores_codex":[0.9990976,0.00001744008,0.0002197332,0.0002066276,0.0001336137,0.0003250148],"domain_scores_gemma":[0.9994203,0.000006586084,0.00002083846,0.0004189065,0.00002516583,0.0001081336],"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":[0.000002788487,0.00003136221,0.00004021074,0.00001290265,0.00002786919,0.00006192718,0.00004533999,0.0002978342,0.974293,0.01974765,0.002446487,0.002992657],"study_design_scores_gemma":[0.0005424136,0.000110018,0.005357857,0.00003460938,0.00006106409,0.0001392657,0.00001108607,0.001825612,0.9251416,0.005696598,0.0604134,0.000666429],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9636384,0.001343942,0.00615887,0.0002996983,0.0002606709,0.0002940769,0.00002000402,0.0009465467,0.02703776],"genre_scores_gemma":[0.9982298,0.0001208417,0.001104954,0.0002836541,0.00007374484,0.00003686439,0.00003386144,0.00003662126,0.00007966242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05796691,"threshold_uncertainty_score":0.8363049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.001800118682464443,"score_gpt":0.1672042097960354,"score_spread":0.165404091113571,"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."}}