{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004471371,0.0002712001,0.0005025525,0.0001114297,0.00007953409,0.00004942531,0.0001813307,0.000393456,0.00001591628],"category_scores_gemma":[0.0006143792,0.0002416406,0.0001450603,0.0002038914,0.0001212756,0.000003137191,0.0001333277,0.0001977672,0.0000185247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004812063,"about_ca_system_score_gemma":0.0002083572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003197413,"about_ca_topic_score_gemma":0.000004332523,"domain_scores_codex":[0.998567,0.00007757219,0.0003864194,0.0005647182,0.0001032846,0.0003009909],"domain_scores_gemma":[0.9991961,0.0001129504,0.0001304623,0.000253821,0.0001682701,0.0001384454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000177588,0.00006819825,0.00004381548,0.001210544,0.00002688568,0.00000160829,0.00001431441,1.151483e-7,0.0009673929,0.00003245272,0.00223131,0.9953856],"study_design_scores_gemma":[0.0002032423,0.00006167744,0.000001447035,0.0004236406,0.00004270436,0.000009632349,0.00005604927,0.000004678296,0.02157879,0.0002636274,0.9770446,0.0003099458],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0005827646,0.9957202,0.001484496,0.00002780026,0.00006866374,0.00170022,0.0001081618,0.00001709201,0.0002905726],"genre_scores_gemma":[0.0002875947,0.991687,0.001923628,0.00002613615,0.0004374462,0.001472698,0.003972271,0.00004447841,0.0001487597],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9950756,"threshold_uncertainty_score":0.9853817,"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."}}