{"id":"W3003623154","doi":"10.69645/knqc9980","title":"What can we learn from conformational profiling of GPCRs?","year":2020,"lang":"en","type":"article","venue":"˜The œbiomedical & life sciences collection.","topic":"Receptor Mechanisms and Signaling","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Profiling (computer programming); G protein-coupled receptor; Computational biology; Chemistry; Computer science; Biology; Receptor; Biochemistry; Programming language","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":[0.0003377926,0.00009405163,0.0001257738,0.00003778149,0.0003406252,0.00006980247,0.0003238697,0.00009699388,0.0003488142],"category_scores_gemma":[0.0001364676,0.00006510558,0.00007420487,0.0005072292,0.0004220493,0.00001683961,0.00009680581,0.0001054182,0.000009965718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009581291,"about_ca_system_score_gemma":0.000441547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000528492,"about_ca_topic_score_gemma":0.00001968005,"domain_scores_codex":[0.9987789,0.00006524694,0.0002762081,0.0002546168,0.00044607,0.0001789574],"domain_scores_gemma":[0.999485,0.00004481151,0.0001193044,0.00009655433,0.00007735059,0.0001769778],"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":[0.0001134684,0.00007252079,0.000450332,0.00003030496,0.00009600782,0.000001080193,0.001744975,0.0007104765,0.9769372,0.0008605064,0.01420818,0.004774932],"study_design_scores_gemma":[0.00169551,0.002239407,0.0002590994,0.0001098625,0.00005839365,0.00001606318,0.02093509,0.06519222,0.7205625,0.002963695,0.1853617,0.0006064008],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8950638,0.003741288,0.07373593,0.02225796,0.003202148,0.0006426694,0.00008989754,0.00005331464,0.001212963],"genre_scores_gemma":[0.9949604,0.0003680737,0.001917728,0.001831087,0.0005972027,0.0000147692,0.00006831333,0.000006464015,0.0002359423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2563747,"threshold_uncertainty_score":0.3819271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02287072544558457,"score_gpt":0.2582954980852935,"score_spread":0.2354247726397089,"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."}}