{"id":"W4385335504","doi":"10.1021/acs.nanolett.3c02186","title":"Toward Predicting Nanoparticle Distribution in Heterogeneous Tumor Tissues","year":2023,"lang":"en","type":"article","venue":"Nano Letters","topic":"Nanoparticle-Based Drug Delivery","field":"Materials Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; NanoMedicines Innovation Network","keywords":"Nanoparticle; Computer science; Identification (biology); Distribution (mathematics); Biological system; Nanotechnology; Tumor cells; Tumor heterogeneity; Materials science; Biology; Mathematics; Cancer; Cancer research","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005799513,0.0001668937,0.0002026111,0.0001031959,0.000110947,0.0001006212,0.0002606618,0.00004326126,0.0001293402],"category_scores_gemma":[0.0001601382,0.0001686991,0.00006955665,0.0006574484,0.00009637435,0.0002836712,0.0001257674,0.00007775748,0.001993478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001789231,"about_ca_system_score_gemma":0.00003253711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000172486,"about_ca_topic_score_gemma":0.00002349959,"domain_scores_codex":[0.9979786,0.0001398563,0.0003803686,0.0004257702,0.0003859385,0.000689433],"domain_scores_gemma":[0.9993502,0.0001214745,0.0000878961,0.0002988878,0.00003130711,0.0001102618],"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":[0.00003776623,0.00004068107,0.01159719,0.00002164814,0.000003399278,0.0002705416,0.0004000123,0.002570764,0.98283,0.00001912294,0.001928593,0.0002802975],"study_design_scores_gemma":[0.000538539,0.00005152282,0.003469142,0.00004564449,0.00001031862,0.00001954309,0.00008436487,0.001571731,0.9933129,0.00004454468,0.000652222,0.0001995283],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958711,0.00005520782,0.00007935343,0.002593227,0.0005688666,0.0002350768,0.00006555227,0.0005187607,0.00001286227],"genre_scores_gemma":[0.9987507,0.000003913324,0.0001068273,0.0008761023,0.0001139279,0.00006187728,0.00003357481,0.00002576205,0.00002734891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01048291,"threshold_uncertainty_score":0.9987836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01864570233999735,"score_gpt":0.2430981055986143,"score_spread":0.2244524032586169,"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."}}