{"id":"W3091422909","doi":"10.1101/2020.10.02.324129","title":"Concentration Sensing in Crowded Environments","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"University of Michigan","keywords":"Crowding; Macromolecular crowding; Biophysics; Biological system; Ligand (biochemistry); Receptor; Dissociation (chemistry); Macromolecule; Dissociation rate; Function (biology); Computer science; Chemistry; Chemical physics; Biology; Biochemistry; Cell biology; Physical chemistry; Neuroscience","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":[0.0005245006,0.000383811,0.0005867402,0.0003440549,0.0005886088,0.0008067064,0.0006726352,0.0008115909,0.0007111599],"category_scores_gemma":[0.001637897,0.0002628571,0.0003497694,0.0002236593,0.0009973604,0.0008193513,0.0007603546,0.0006766459,0.0001875891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001045324,"about_ca_system_score_gemma":0.0006315795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005036484,"about_ca_topic_score_gemma":0.00159478,"domain_scores_codex":[0.999701,0.00006752191,0.0000132758,0.00008055975,0.00009956229,0.00003812719],"domain_scores_gemma":[0.999316,0.0003973679,0.00008447294,0.00005431766,0.0000948378,0.00005305879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006914545,0.00003240967,0.0008005025,0.00003943194,0.00001720707,0.0001345213,0.0000828696,0.9535642,0.03085343,0.0106334,0.0003394635,0.003433461],"study_design_scores_gemma":[0.000007582865,0.00001161799,0.0001129259,0.000003105666,0.000002174946,0.000009922012,0.000007405082,0.9911711,0.006352847,0.001885795,0.0004261995,0.00000944678],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4874747,0.0005310004,0.503867,0.0004959811,0.0001411323,0.00009457799,0.0002373624,0.0007552056,0.006403048],"genre_scores_gemma":[0.9465945,0.0002107081,0.05098588,0.000105457,0.00002357455,0.00007315663,0.0001255166,0.00005417875,0.001826989],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005036484,"threshold_uncertainty_score":0.0100143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01014765712385466,"score_gpt":0.2356999419878237,"score_spread":0.225552284863969,"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."}}