{"id":"W2793263492","doi":"10.3389/fbioe.2018.00017","title":"A Novel Methodology for Characterizing Cell Subpopulations in Automated Time-lapse Microscopy","year":2018,"lang":"en","type":"article","venue":"Frontiers in Bioengineering and Biotechnology","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Deutsche Forschungsgemeinschaft","keywords":"Preprocessor; Tracking (education); Spurious relationship; Computer science; Biological system; Particle (ecology); Noise (video); Computer vision; Artificial intelligence; Video microscopy; Lineage (genetic); Pattern recognition (psychology); Image (mathematics); Chemistry; Biology; Cell biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0002852437,0.0001684569,0.0002769649,0.0005196662,0.00003870406,0.00001346466,0.0001706861,0.0005169531,0.000001621814],"category_scores_gemma":[0.0001131567,0.0001827721,0.00004733407,0.0003125134,0.0001759336,0.000006019955,0.0001021716,0.000129475,0.000001133328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002827755,"about_ca_system_score_gemma":0.00001616736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003673166,"about_ca_topic_score_gemma":0.0000346855,"domain_scores_codex":[0.9989519,0.00002492065,0.0002576064,0.0004119322,0.00002974345,0.000323928],"domain_scores_gemma":[0.9995877,0.00001322458,0.00006282439,0.0002738484,0.00003236719,0.00003005041],"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":[0.00003743459,0.00004101032,0.00218171,0.00002088485,0.00001833401,0.000001372121,0.000024385,0.00001108722,0.9948416,0.00001354878,0.001432211,0.001376433],"study_design_scores_gemma":[0.0005172499,0.0002639325,0.001026711,0.00001862687,0.00001377385,0.00001407597,0.00003635271,0.02478104,0.9637184,0.00004292829,0.009357321,0.000209558],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6855578,0.0005562577,0.3130543,0.0002638855,0.0001064525,0.0002364138,0.00001173572,0.0001920299,0.00002116535],"genre_scores_gemma":[0.5677872,0.0002424612,0.4315453,0.00008469816,0.00006092944,0.00005116072,0.00009665071,0.00002738105,0.0001042011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.118491,"threshold_uncertainty_score":0.745323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01137748923042894,"score_gpt":0.270759753515425,"score_spread":0.2593822642849961,"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."}}