{"id":"W3027933648","doi":"10.1016/j.tifs.2020.05.010","title":"Going deep inside bioactive-loaded nanocarriers through Nuclear Magnetic Resonance (NMR) spectroscopy","year":2020,"lang":"en","type":"article","venue":"Trends in Food Science & Technology","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Nanocarriers; Nuclear magnetic resonance spectroscopy; Spectroscopy; Chemistry; Characterization (materials science); Analytical Chemistry (journal); Nuclear magnetic resonance; Materials science; Nanotechnology; Chemical physics; Drug delivery; Chromatography; Organic chemistry; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009512429,0.000209911,0.0002562742,0.0004416055,0.0003689486,0.00006331936,0.0009311624,0.0000871568,0.0003509057],"category_scores_gemma":[0.00002081613,0.0002169018,0.000060077,0.005539865,0.001337969,0.0003214845,0.0002395279,0.0004704522,0.0000987483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009858574,"about_ca_system_score_gemma":0.0000930237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003191402,"about_ca_topic_score_gemma":0.00001585274,"domain_scores_codex":[0.9980966,0.00001809482,0.0002718495,0.0007371899,0.0002144968,0.0006617843],"domain_scores_gemma":[0.9992799,0.0000190138,0.0001041796,0.0004606901,0.00003201282,0.0001041692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002739079,0.000111008,0.006865861,0.000002881119,0.000007972237,0.000003817909,0.001337412,0.00003192121,0.3679368,0.5786153,0.0001730005,0.04488666],"study_design_scores_gemma":[0.001147941,0.001387175,0.002970921,0.00003291439,0.00002053687,0.000005930311,0.002902678,0.002193353,0.8540819,0.08823533,0.04637351,0.000647783],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9245676,0.0007026591,0.004095949,0.01058912,0.0001087345,0.0002484702,0.00003066659,0.0004250853,0.05923166],"genre_scores_gemma":[0.9899123,0.000008793249,0.009653685,0.0002448656,0.00006562511,0.00004937396,0.000002624919,0.00002044419,0.00004229963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.49038,"threshold_uncertainty_score":0.8844997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0180500849847905,"score_gpt":0.3103295340054082,"score_spread":0.2922794490206177,"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."}}