{"id":"W3080935191","doi":"","title":"Protein Proximity Mapping: Getting to Know Your Neighbors","year":2020,"lang":"en","type":"article","venue":"PubMed Central","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute","funders":"","keywords":"Computer science; Data science; Data mining; Information retrieval","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":[0.002162285,0.0009278065,0.0009918493,0.001389562,0.001731218,0.002974224,0.001464065,0.001849897,0.02183702],"category_scores_gemma":[0.005648718,0.0008142689,0.0005270721,0.001683429,0.00125382,0.00886524,0.002376049,0.002627922,0.01819591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000845318,"about_ca_system_score_gemma":0.001178699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007678761,"about_ca_topic_score_gemma":0.001728428,"domain_scores_codex":[0.9987041,0.0002955396,0.00005816667,0.0002391085,0.0006129968,0.00009005311],"domain_scores_gemma":[0.998293,0.0005859889,0.0001181549,0.0002836353,0.0003829534,0.0003362562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003717823,0.0001102501,0.001703392,0.001819855,0.0000644895,0.0004764229,0.001143786,0.0007731149,0.07288947,0.04134295,0.2016324,0.677672],"study_design_scores_gemma":[0.00004564083,0.0001855598,0.001047702,0.0003057568,0.00005769926,0.001339456,0.0008732164,0.001620907,0.03878105,0.04391662,0.9117454,0.00008113515],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03228905,0.1420998,0.6342909,0.06422553,0.01681467,0.0004355264,0.002134342,0.01356722,0.09414291],"genre_scores_gemma":[0.1103613,0.1045823,0.673439,0.01058241,0.004520565,0.000421616,0.002932666,0.00318995,0.08997024],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02183702,"threshold_uncertainty_score":0.07305211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02497796731309462,"score_gpt":0.2462412326390683,"score_spread":0.2212632653259737,"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."}}