{"id":"W1973899346","doi":"10.1039/b812443d","title":"An integrated microfluidic chip for chromosome enumeration using fluorescence in situ hybridization","year":2008,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Microfluidic and Capillary Electrophoresis Applications","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Microfluidic chip; Fluorescence in situ hybridization; Fish <Actinopterygii>; Microfluidics; Chip; In situ; Reagent; In situ hybridization; Lab-on-a-chip; Fluorescence; Chromosome; Computer science; Computer hardware; Nanotechnology; Chemistry; Biology; Materials science; Genetics; Fishery; Gene; Gene expression; Physics; Telecommunications","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":[0.0000917255,0.0001739229,0.0001662721,0.0001456704,0.0001373872,0.00002580252,0.0001376743,0.00008944274,0.00002511047],"category_scores_gemma":[0.00001647015,0.000183415,0.0000391448,0.0004050888,0.00003048508,0.0001373176,0.000005770932,0.0001388094,0.00002101217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001592549,"about_ca_system_score_gemma":0.00005579038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002961689,"about_ca_topic_score_gemma":0.000009908104,"domain_scores_codex":[0.9990951,0.00003492598,0.0002527476,0.0002489955,0.0001038735,0.0002643042],"domain_scores_gemma":[0.9995771,0.00002328523,0.00003242215,0.0002543899,0.00004735228,0.00006544486],"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":[0.0000203744,0.00005849176,0.000220544,0.00002050989,0.000007554217,0.000002808426,0.0002721727,0.0003018599,0.9943943,0.0004785314,0.003491568,0.0007312954],"study_design_scores_gemma":[0.0004867923,0.0000928126,0.002059068,0.00004139361,0.000009726334,0.00002572719,0.00002721605,0.01604791,0.9743302,0.00009302144,0.006532754,0.00025332],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9746789,0.004218409,0.02022843,0.00002904826,0.00007731919,0.0004409621,0.00002587757,0.000180589,0.0001204663],"genre_scores_gemma":[0.9903963,0.008305123,0.0006456596,0.00009585395,0.0001082274,0.00007881696,0.0002991003,0.00004703741,0.00002392993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02006403,"threshold_uncertainty_score":0.7479448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01453710656697158,"score_gpt":0.2290894227145703,"score_spread":0.2145523161475987,"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."}}