{"id":"W4386526082","doi":"10.1101/2023.09.05.556421","title":"Modelling Microtube Driven Invasion of Glioma","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Division of Mathematical Sciences; Natural Sciences and Engineering Research Council of Canada; Istituto Nazionale di Alta Matematica \"Francesco Severi\"; Gruppo Nazionale per la Fisica Matematica; Alberta Innovates; Dipartimenti di Eccellenza; Politecnico di Torino","keywords":"Glioma; Context (archaeology); Computer science; Population; Biological system; Statistical physics; Scaling; Applied mathematics; Mechanics; Physics; Mathematics; Biology; Medicine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001225103,0.0007282562,0.001337241,0.000522966,0.0001167888,0.00007351096,0.001116356,0.0009508362,0.00006838352],"category_scores_gemma":[0.001190739,0.000717152,0.0003683352,0.000645514,0.0002599492,0.00009069625,0.001343366,0.0009995457,0.0002205534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001855759,"about_ca_system_score_gemma":0.0003576277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003587862,"about_ca_topic_score_gemma":0.000001468107,"domain_scores_codex":[0.9960642,0.0002392414,0.001311888,0.0011243,0.0005540747,0.0007062361],"domain_scores_gemma":[0.9953488,0.000752737,0.0009668126,0.002095359,0.0005679794,0.0002683069],"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":[0.00004678413,0.0004559503,0.001328517,0.005745804,0.0004440977,0.0001897221,0.00005211679,0.0007014848,0.9316941,0.05789293,0.001448198,2.747913e-7],"study_design_scores_gemma":[0.0008949217,0.0001225605,0.0009484499,0.003498779,0.0004919726,9.964193e-8,0.000009707955,0.03040766,0.9537905,0.007921447,0.0002157479,0.001698186],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9643264,0.0001712244,0.03230238,0.0001691202,0.0007317516,0.001124389,0.000261878,0.0008877849,0.00002506568],"genre_scores_gemma":[0.9107285,0.00007754251,0.08845287,0.00002911527,0.0002422187,0.0001887732,4.487326e-7,0.0002674872,0.00001307287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05615049,"threshold_uncertainty_score":0.9995279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06040515955982924,"score_gpt":0.2653868892650675,"score_spread":0.2049817297052383,"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."}}