{"id":"W2908499227","doi":"10.1007/978-1-4939-9036-8_4","title":"Quantification of T-Cell Migratory Phenotypes Using High-Content Analysis","year":2019,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Lee Kong Chian School of Medicine, Nanyang Technological University; Ministry of Education, India; Ministry of Earth Sciences; Nanyang Technological University","keywords":"Phenotype; Biology; Population; High-content screening; Computational biology; Cell; Cell biology; Genetics; Medicine; Gene","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":[0.0006596728,0.0009927418,0.0007032278,0.002402198,0.001187589,0.001281537,0.0007311681,0.0007697876,0.004888249],"category_scores_gemma":[0.0003548345,0.0004117134,0.0006337062,0.002531674,0.0005922311,0.0006347353,0.0005859849,0.00195774,0.002009047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007073868,"about_ca_system_score_gemma":0.0006121688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001574156,"about_ca_topic_score_gemma":0.00336695,"domain_scores_codex":[0.9990115,0.0001263607,0.00008170787,0.0001911866,0.0003700072,0.0002191394],"domain_scores_gemma":[0.9992938,0.0001084156,0.0001337881,0.00009137856,0.0002721986,0.0001004183],"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":[0.0001127743,0.0000623263,0.0002635006,0.00009385608,0.00001990855,0.0000174768,0.0000451729,0.00009005526,0.9941506,0.0003262614,0.0002110087,0.004606988],"study_design_scores_gemma":[0.00001548105,0.00007272138,0.006009441,0.00001469754,0.00003918231,0.00008487424,0.00003922957,0.003741227,0.9875924,0.0001477066,0.002224904,0.00001814039],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5371099,0.002237612,0.4347988,0.0004144052,0.0003186523,0.0009098357,0.00780187,0.00386438,0.01254453],"genre_scores_gemma":[0.4559944,0.003131713,0.5132418,0.0004216423,0.0001227866,0.002930742,0.007157154,0.001613174,0.01538657],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004888249,"threshold_uncertainty_score":0.01635277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03170586595400255,"score_gpt":0.3751155339907769,"score_spread":0.3434096680367743,"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."}}