{"id":"W3195311165","doi":"10.1126/sciadv.abe6984","title":"Quantification of fast molecular adhesion by fluorescence footprinting","year":2021,"lang":"en","type":"article","venue":"Science Advances","topic":"Force Microscopy Techniques and Applications","field":"Physics and Astronomy","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus","funders":"Natural Sciences and Engineering Research Council of Canada; Michael Smith Health Research BC; Canada Foundation for Innovation","keywords":"Footprinting; Adhesion; DNA; Cell adhesion molecule; Cell adhesion; Fluorescence; Biophysics; DNA footprinting; Measure (data warehouse); Chemistry; Nanotechnology; Computational biology; Biology; Materials science; Computer science; Cell biology; Gene; Biochemistry; Base sequence; Physics; DNA-binding protein; Data mining; Transcription factor","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000114036,0.00005477361,0.00006855842,0.00002467229,0.0001428365,0.00003334582,0.0002183046,0.000008252162,0.00003345323],"category_scores_gemma":[0.000008715486,0.00005075641,0.00002971557,0.0005027832,0.000247769,0.0002164377,0.0000563245,0.00004159037,0.000004708904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007903902,"about_ca_system_score_gemma":0.00006598566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002084611,"about_ca_topic_score_gemma":2.564973e-7,"domain_scores_codex":[0.9993448,0.000006726762,0.0001307777,0.0002343662,0.0001468508,0.0001364415],"domain_scores_gemma":[0.999508,0.00001069603,0.00009213646,0.0002235421,0.0001346114,0.00003104701],"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":[3.71906e-7,0.00002579782,0.002106153,0.000003854289,6.133116e-7,5.509356e-8,0.0000225225,0.0000227884,0.9314761,0.02500395,0.00002608694,0.04131173],"study_design_scores_gemma":[0.00002602803,0.000008093461,0.0002624159,0.00002209392,0.000002033798,1.832374e-7,0.0002864885,0.0001103004,0.9928805,0.003052509,0.00329213,0.00005723388],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7609274,0.0003770683,0.2326646,0.0001788226,0.00005589633,0.0001191831,0.00001483044,0.00003400997,0.005628236],"genre_scores_gemma":[0.9750789,0.000007698647,0.02474475,0.000007782302,0.000009072995,0.00001379811,0.000008213154,0.000003398763,0.000126372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2141515,"threshold_uncertainty_score":0.2069786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00765033132626402,"score_gpt":0.298977862403235,"score_spread":0.291327531076971,"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."}}