{"id":"W3125320539","doi":"10.1101/2021.01.19.427319","title":"AutoSmarTrace: Automated Chain Tracing and Flexibility Analysis of Biological Filaments","year":2021,"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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Colorado Boulder; University of California, San Francisco; Shriners Hospitals for Children","keywords":"Persistence length; Tracing; Flexibility (engineering); Computer science; Ray tracing (physics); Algorithm; Artificial intelligence; Ground truth; Biological system; Optics; Physics; Mathematics; Molecule; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005748668,0.0004682271,0.0008032938,0.0002450135,0.00008571527,0.00007632134,0.0003607703,0.0008646457,0.00001855121],"category_scores_gemma":[0.0003654693,0.0004879158,0.0002620744,0.0006147439,0.0003096782,0.000009846374,0.0007966804,0.0003895565,6.4442e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008011776,"about_ca_system_score_gemma":0.0002528298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003183888,"about_ca_topic_score_gemma":0.000005479606,"domain_scores_codex":[0.9972692,0.0002091995,0.0006150636,0.001300913,0.0001974209,0.0004081613],"domain_scores_gemma":[0.9977349,0.00002630476,0.0004417131,0.001258593,0.0003782402,0.0001602399],"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":[0.00003495193,0.0001366629,0.02569164,0.000129375,0.0006732997,0.00001222362,0.000006881178,0.0002839607,0.9729836,0.00001283155,0.00003055471,0.000004016958],"study_design_scores_gemma":[0.000154569,0.00008108788,0.1537078,0.0001247534,0.0002994878,1.457012e-8,0.000004688045,0.002879303,0.8422241,5.259036e-7,0.00010747,0.0004161897],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9792351,0.002228227,0.01729013,0.00002325611,0.0001377072,0.0004225373,0.0003403824,0.0003207273,0.000001976404],"genre_scores_gemma":[0.9501088,0.0008018541,0.04882659,0.0000667106,0.00005751767,0.00007791747,0.00001011123,0.0000487083,0.000001755394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1307595,"threshold_uncertainty_score":0.9997572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01571474991091744,"score_gpt":0.276799196237296,"score_spread":0.2610844463263786,"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."}}