{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005053495,0.0001857878,0.0001572397,0.00001734045,0.0001358166,0.00005306059,0.0003785625,0.0003790418,0.004289464],"category_scores_gemma":[0.0001138686,0.000186249,0.0000777123,0.0001365486,0.0001506235,0.00006103802,0.0001185699,0.0003466901,0.0001932866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002176254,"about_ca_system_score_gemma":0.00003687669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002343219,"about_ca_topic_score_gemma":0.000003328958,"domain_scores_codex":[0.9989377,0.00001062313,0.0001937413,0.0003256584,0.0002053071,0.0003269682],"domain_scores_gemma":[0.9991326,0.00003415278,0.0000664885,0.0006102367,0.00005290183,0.0001036391],"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":[0.00002863898,0.00005509965,0.0001009015,0.0000397071,0.00002795137,0.0000413146,0.00007635498,9.442321e-7,0.9971292,0.0003367022,0.0008688132,0.00129436],"study_design_scores_gemma":[0.0002616681,0.00002001774,0.000008942924,0.00006489126,0.00001290137,0.00002395671,0.00006269696,0.00004461393,0.9403376,0.001484678,0.05745697,0.0002210763],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9110624,0.000193714,0.001035104,0.0001755376,0.00005340045,0.00003798237,0.00001443333,0.0001677317,0.08725972],"genre_scores_gemma":[0.9903632,0.000006906607,0.001263049,0.00029983,0.0008064318,0.00002182262,0.00004409445,0.00003208603,0.007162513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08009721,"threshold_uncertainty_score":0.9966208,"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."}}