{"id":"W2985083478","doi":"10.1016/j.cmpb.2019.105165","title":"Individualized growth prediction of mice skin tumors with maximum likelihood estimators","year":2019,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"European Research Council; Central Intelligence Agency; Nvidia","keywords":"Gompertz function; Estimator; Maximum a posteriori estimation; Mathematics; Statistics; Maximum likelihood; Applied mathematics","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":[0.001102359,0.0004112228,0.000561028,0.0005660259,0.0001803699,0.0005480614,0.0005875615,0.0006643542,0.000634208],"category_scores_gemma":[0.004803416,0.0004124563,0.0006065481,0.0003812398,0.0002976441,0.0006097993,0.000646195,0.0008074267,0.0002420657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005527154,"about_ca_system_score_gemma":0.0005173943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001734922,"about_ca_topic_score_gemma":0.001891722,"domain_scores_codex":[0.9996709,0.0001425149,0.00001359218,0.00007638587,0.00006876142,0.00002795464],"domain_scores_gemma":[0.9978758,0.001538577,0.0002604642,0.0001376354,0.0001307668,0.0000567438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001833661,0.00004747774,0.005499776,0.00002916963,0.00003834239,0.00006938977,0.00004579671,0.9393883,0.004520691,0.004721813,0.0005738018,0.04488203],"study_design_scores_gemma":[0.000002681575,0.000008932449,0.0004021427,0.000001322738,0.000003074778,0.00001040265,0.000002369847,0.9972678,0.000847273,0.001399119,0.0000514538,0.000003490695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09058507,0.0001059499,0.9082354,0.0001251345,0.000007952503,0.00001645608,0.00009154275,0.0004206038,0.0004117851],"genre_scores_gemma":[0.8752216,0.00009532584,0.1224278,0.0000379112,0.00001667864,0.00007701074,0.0003518434,0.0001128244,0.001659005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001734922,"threshold_uncertainty_score":0.005829871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04019030946971763,"score_gpt":0.3364633445941565,"score_spread":0.2962730351244389,"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."}}