{"id":"W2606854312","doi":"10.11159/icnnfc17.120","title":"Analytical Developments for the Characterization of Nanomaterials inConsumer Products, Environmental and Medicinal Samples","year":2017,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Recent Advances in Nanotechnology","topic":"Nanoparticles: synthesis and applications","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Horizon 2020","keywords":"Characterization (materials science); Nanomaterials; Nanotechnology; Materials science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003812145,0.0001074165,0.0002365447,0.00007971958,0.0002458719,0.00003261869,0.000654087,0.00004965397,0.00002570588],"category_scores_gemma":[0.0003979403,0.00006406446,0.00001892823,0.00009050657,0.0008914379,0.0001847438,0.000232116,0.00005454544,0.000001270578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002646919,"about_ca_system_score_gemma":0.00001632226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000152203,"about_ca_topic_score_gemma":0.000006276848,"domain_scores_codex":[0.9990653,0.000007608118,0.000355415,0.0002523786,0.0001472904,0.0001719832],"domain_scores_gemma":[0.9989979,0.0001132492,0.0005900442,0.0002262013,0.00005701416,0.00001556175],"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":[0.00009085257,0.0000591628,0.01630019,0.00004476007,0.000006316564,3.818958e-8,0.00002113133,3.388222e-7,0.9372773,0.01192878,0.000009274218,0.03426183],"study_design_scores_gemma":[0.0002871064,0.00002783933,0.01688813,0.0001114632,0.0000201981,0.000001742874,0.00003971513,0.00002587432,0.9547377,0.001075609,0.02671563,0.00006905956],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939609,0.0003831001,0.000007322835,0.00461773,0.0003458924,0.0005988161,0.00003853992,0.00001238875,0.00003536813],"genre_scores_gemma":[0.9970937,0.001870253,0.0007309287,0.00002900975,0.00002161803,0.0001626748,0.000001221294,0.000008051042,0.00008256493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03419277,"threshold_uncertainty_score":0.328454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01941295713939371,"score_gpt":0.27628959277687,"score_spread":0.2568766356374763,"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."}}