{"id":"W2936891675","doi":"10.1101/614966","title":"A How-To guide to: Quantitative high-speed video profiling to discriminate between variants of primary ciliary dyskinesia","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic and Kidney Cyst Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"Canadian Lung Association","keywords":"Primary ciliary dyskinesia; Cilium; Profiling (computer programming); Computer science; Motile cilium; Complement (music); Video microscopy; Artificial intelligence; Computational biology; Neuroscience; Biology; Medicine; Genetics; Phenotype; Cell biology; Lung; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002476181,0.002340746,0.001536137,0.00298158,0.0008837099,0.003626819,0.002265182,0.002897806,0.1982193],"category_scores_gemma":[0.009661165,0.001551389,0.001629788,0.001988661,0.0005344435,0.001958782,0.002548883,0.00283551,0.249307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006075155,"about_ca_system_score_gemma":0.001419325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001848232,"about_ca_topic_score_gemma":0.002961297,"domain_scores_codex":[0.9985266,0.0003040004,0.0001352544,0.0003271941,0.0005812554,0.0001257663],"domain_scores_gemma":[0.9954828,0.001607736,0.0001871976,0.001449818,0.0009119486,0.0003605652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001662037,0.0001137496,0.0008805718,0.0005847662,0.00008109102,0.0001773599,0.000075983,0.0007822636,0.01113459,0.003373472,0.8704122,0.1122178],"study_design_scores_gemma":[0.0001634949,0.00008904283,0.002225328,0.000533106,0.00004480503,0.0005642658,0.00007104911,0.009068364,0.03139889,0.01469014,0.940959,0.0001924848],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002742506,0.002648765,0.5175083,0.005493239,0.00561227,0.001304748,0.1356076,0.3016135,0.02746905],"genre_scores_gemma":[0.01740941,0.004997883,0.6665284,0.004936938,0.002134633,0.003893086,0.1318878,0.08017307,0.08803888],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1982193,"threshold_uncertainty_score":0.6631095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01495517225040948,"score_gpt":0.2471779301724681,"score_spread":0.2322227579220586,"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."}}