{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008203804,0.0001577589,0.0001462263,0.00001710403,0.0001052033,0.00004433366,0.0003014636,0.0000828193,0.0001495622],"category_scores_gemma":[0.0001839096,0.000164369,0.00006656544,0.0001993154,0.00002342142,0.00007522527,0.000127384,0.0002685901,0.00002563506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001501434,"about_ca_system_score_gemma":0.00004879206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008076528,"about_ca_topic_score_gemma":0.0000011846,"domain_scores_codex":[0.9982781,0.000007078645,0.0002259783,0.0003789638,0.000143648,0.0009662678],"domain_scores_gemma":[0.9989802,0.000008724162,0.00008623556,0.0002439934,0.0000276928,0.0006531994],"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":[0.0001138139,0.0002344301,0.02046508,0.0004508959,0.00005735719,0.00002034325,0.00219768,0.0003515453,0.8175712,0.0168851,0.005375523,0.136277],"study_design_scores_gemma":[0.0002781617,0.00001138337,0.001605374,0.00003463379,0.000008936562,0.00000284987,0.0001453852,0.0008894915,0.7446078,0.001692747,0.2503358,0.0003873755],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8678758,0.00007507392,0.09523737,0.01814161,0.00004388865,0.001957994,0.00007002405,0.0009989518,0.01559925],"genre_scores_gemma":[0.9227127,0.000005349477,0.07245254,0.0008800518,0.0007075886,0.002361428,0.00002080062,0.0000409349,0.0008185997],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2449603,"threshold_uncertainty_score":0.6702774,"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."}}