{"id":"W1971742510","doi":"10.1002/cbic.201402549","title":"Sugar Recognition: Designing Artificial Receptors for Applications in Biological Diagnostics and Imaging","year":2015,"lang":"en","type":"review","venue":"ChemBioChem","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada","keywords":"Sugar; Receptor; Molecular recognition; Folding (DSP implementation); Computational biology; Computer science; Biochemistry; Nanotechnology; Chemistry; Biology; Engineering; Materials science; Molecule","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005967725,0.000897415,0.000892266,0.001776118,0.0002278592,0.0009445412,0.0009936761,0.001339187,0.001951022],"category_scores_gemma":[0.000463772,0.0003954328,0.0004079177,0.001714415,0.0005987892,0.001857229,0.0008230872,0.001789999,0.002167332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007047719,"about_ca_system_score_gemma":0.0005288041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007231054,"about_ca_topic_score_gemma":0.001113553,"domain_scores_codex":[0.9998342,0.0000257261,0.00001563657,0.0000302069,0.00006955307,0.00002462781],"domain_scores_gemma":[0.999881,0.0000480929,0.00001974613,0.000005628872,0.0000317725,0.00001361542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004860365,0.00007478621,0.0001301495,0.0126512,0.00005602201,0.000271348,0.00009630492,0.0007498501,0.01219159,0.01486421,0.02463784,0.9342281],"study_design_scores_gemma":[0.000009156864,0.00005382147,0.0002044118,0.001106333,0.00003358077,0.0007519667,0.00004114363,0.0001605967,0.002188163,0.002500738,0.9929305,0.00001946474],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003014854,0.9965317,0.000765361,0.0002542452,0.0002373875,0.000008717948,0.00002018105,0.00001522998,0.001865799],"genre_scores_gemma":[0.001528232,0.9958802,0.0008397527,0.0002553979,0.00009846908,0.00001397924,0.00003238698,0.000003121495,0.001348328],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.001951022,"threshold_uncertainty_score":0.006526768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1308620536321663,"score_gpt":0.3799185640195671,"score_spread":0.2490565103874008,"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."}}