{"id":"W2883680429","doi":"10.1021/acsnano.8b02856","title":"Crystalline Cyclophane–Protein Cage Frameworks","year":2018,"lang":"en","type":"article","venue":"ACS Nano","topic":"Supramolecular Chemistry and Complexes","field":"Chemistry","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta; Academy of Finland; Sigrid Juséliuksen Säätiö; Suomen Kulttuurirahasto; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Supramolecular chemistry; Biomolecule; Cyclophane; Molecular recognition; Non-covalent interactions; Nanotechnology; Cationic polymerization; Materials science; Self-assembly; Host–guest chemistry; Chemistry; Molecule; Crystallography; Crystal structure; Polymer chemistry; Hydrogen bond; Organic chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005531004,0.0002023813,0.0001403101,0.000172754,0.0002259926,0.0003418618,0.0003534415,0.0003195821,0.002498355],"category_scores_gemma":[0.0001109698,0.0001553112,0.0001300451,0.000159833,0.0002089793,0.0003086898,0.0002521829,0.0003008968,0.0003101019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000388355,"about_ca_system_score_gemma":0.0001881528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008786567,"about_ca_topic_score_gemma":0.001823613,"domain_scores_codex":[0.9999304,0.000006584589,0.000003045503,0.0000214034,0.00001998915,0.0000185316],"domain_scores_gemma":[0.9999552,0.00001073772,0.00001046836,0.000006732489,0.000005717207,0.00001113812],"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":[0.0001720428,0.0001226618,0.0004292511,0.0002782473,0.00003540438,0.0003427984,0.0001068727,0.007096678,0.9603243,0.01946273,0.001736949,0.009892003],"study_design_scores_gemma":[0.0001822725,0.0007699691,0.002214239,0.00003655519,0.00004611499,0.000521353,0.0001193074,0.03248258,0.92175,0.003159677,0.03866482,0.00005300686],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9679547,0.002207108,0.01325126,0.0003450221,0.00008271328,0.00008374209,0.0006508385,0.0004607181,0.01496395],"genre_scores_gemma":[0.9905972,0.0008437184,0.00597668,0.00005719428,0.00001045795,0.00005107946,0.0003275041,0.00002536007,0.002110813],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002498355,"threshold_uncertainty_score":0.008357823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008062459337804015,"score_gpt":0.2302360934140465,"score_spread":0.2221736340762425,"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."}}