{"id":"W2795656389","doi":"10.1101/294157","title":"A Stochastic Model for Cancer Metastasis: Branching Stochastic Process with Settlement","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Uniqueness; Mathematics; Branching process; Stochastic differential equation; Extinction probability; Stochastic modelling; Stochastic process; Branching (polymer chemistry); Differential equation; Distribution (mathematics); Applied mathematics; Statistical physics; Statistics; Mathematical analysis; Physics; Medicine; Chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001287368,0.0009248124,0.001148256,0.001074501,0.000731495,0.001595996,0.002116777,0.002676683,0.003205262],"category_scores_gemma":[0.003053298,0.0003570353,0.001351793,0.001086176,0.001868907,0.001802066,0.001670144,0.002102168,0.0005188452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001621484,"about_ca_system_score_gemma":0.0009559108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004079225,"about_ca_topic_score_gemma":0.002317634,"domain_scores_codex":[0.9991897,0.0002079644,0.00004059386,0.0001773184,0.0002545888,0.0001299032],"domain_scores_gemma":[0.9986085,0.0005614995,0.0003514629,0.00008485228,0.0001841917,0.0002095866],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004920298,0.00005337593,0.002229611,0.0001000902,0.00003583914,0.0006613148,0.0002337019,0.3402337,0.0103497,0.6390091,0.001835594,0.005208851],"study_design_scores_gemma":[0.00004427835,0.00004647737,0.0004602658,0.00001143125,0.00002400485,0.0003070969,0.00003533412,0.873696,0.0005159357,0.1229083,0.001921264,0.00002956838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0756105,0.0008590336,0.9094511,0.002214688,0.0002241344,0.00010182,0.0004111502,0.0001819617,0.01094566],"genre_scores_gemma":[0.9238874,0.001336077,0.05029421,0.0005069228,0.0004505867,0.0003415892,0.0004260184,0.00008560726,0.02267165],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004079225,"threshold_uncertainty_score":0.01176476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0407977646234431,"score_gpt":0.2982242426597005,"score_spread":0.2574264780362575,"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."}}