{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000178296,0.0004174463,0.0003350646,0.00006055746,0.00005902052,0.00007251648,0.0003368026,0.0005863688,0.000006305496],"category_scores_gemma":[0.0001462655,0.000517494,0.00009012173,0.0001436908,0.0001343501,0.00000989579,0.0005091939,0.0005084049,0.00001757528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001683871,"about_ca_system_score_gemma":0.0002289377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001506914,"about_ca_topic_score_gemma":0.000002083939,"domain_scores_codex":[0.9978752,0.0001028599,0.0004087434,0.001008508,0.0001887002,0.0004159909],"domain_scores_gemma":[0.9987351,0.000007377939,0.0002530918,0.0007996777,0.00005728009,0.0001474304],"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":[0.00003933141,0.00003908684,0.003040738,0.00005904889,0.00002744252,0.00003126421,0.000004274401,0.00006406007,0.9964983,0.00002644586,0.0001666136,0.000003399074],"study_design_scores_gemma":[0.0003027605,0.0000537998,0.009856415,0.0001482946,0.00001817929,1.068438e-8,0.000001295596,0.000254346,0.9844957,0.00000333148,0.004368279,0.0004975675],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8981585,0.0009914797,0.099072,0.0001972231,0.0004018464,0.000911248,0.0001098485,0.0001479095,0.000009942954],"genre_scores_gemma":[0.9480387,0.0004854814,0.05071347,0.0003480684,0.0002670523,0.00004144673,0.00000300009,0.0001001878,0.000002612153],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04988018,"threshold_uncertainty_score":0.9997277,"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."}}