{"id":"W2404211923","doi":"10.2174/1386207319666160408150649","title":"An Analysis of the Practicalities of Multi-Color Nanoparticle Cellular Bar-Coding","year":2016,"lang":"en","type":"article","venue":"Combinatorial Chemistry & High Throughput Screening","topic":"graph theory and CDMA systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council","keywords":"Bar (unit); Coding (social sciences); Color-coding; Computer science; Nanoparticle; Nanotechnology; Materials science; Artificial intelligence; Mathematics; Physics; Statistics","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":[0.001756664,0.0005278244,0.0003657366,0.0005474874,0.0003352645,0.0007618167,0.0008432544,0.0008723302,0.002045813],"category_scores_gemma":[0.01258488,0.000276179,0.0003359328,0.0004631635,0.0009962427,0.00122751,0.0005011277,0.0007249094,0.0002597618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001347319,"about_ca_system_score_gemma":0.0006264717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002012117,"about_ca_topic_score_gemma":0.001559916,"domain_scores_codex":[0.9989544,0.0003812671,0.00002494498,0.000144513,0.0003717711,0.0001231704],"domain_scores_gemma":[0.984687,0.01289236,0.0005162628,0.001123066,0.0006664834,0.000114787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004892825,0.0003325061,0.002658208,0.0003714029,0.00004890929,0.0002913621,0.0001198227,0.738243,0.1188878,0.06786447,0.000755787,0.06993734],"study_design_scores_gemma":[0.00001117942,0.0002063495,0.0005878983,0.00001033485,0.00001818726,0.0001221205,0.00002710813,0.9406536,0.05078301,0.006609039,0.00095365,0.00001745722],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5096626,0.001504514,0.4727892,0.0009885118,0.00006741148,0.0001803187,0.0001511741,0.0005491441,0.01410714],"genre_scores_gemma":[0.9573677,0.0003423335,0.04052099,0.0000459268,0.00001191264,0.00005628138,0.00004456395,0.00003623355,0.001574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002045813,"threshold_uncertainty_score":0.009775519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01374001462135842,"score_gpt":0.2287628188804113,"score_spread":0.2150228042590529,"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."}}