{"id":"W6887395092","doi":"10.15562/tcp.76","title":"Machine learning application in cancer research: Mini Review","year":2019,"lang":"en","type":"article","venue":"The Cancer Press","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Cancer; Feature (linguistics); Process (computing); Field (mathematics); Training set","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001525414,0.00009979081,0.000159511,0.00006014274,0.0001055248,0.00005473939,0.00132256,0.00004250355,0.00008434362],"category_scores_gemma":[0.00003417405,0.00007411715,0.00003232684,0.0006703385,0.00004927093,0.0002691002,0.0003689961,0.0005487562,0.00007053243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003420864,"about_ca_system_score_gemma":0.0001116995,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008793292,"about_ca_topic_score_gemma":0.00100061,"domain_scores_codex":[0.9982671,0.0003629821,0.0002000369,0.0004299038,0.0004311453,0.0003088559],"domain_scores_gemma":[0.9987502,0.0001602589,0.0001075177,0.0008231851,0.000119679,0.00003919955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005017599,0.00005025236,0.009027005,0.001391368,0.00004279869,0.000002255174,0.002109798,0.01248609,0.003765277,0.005944696,0.009648007,0.9554823],"study_design_scores_gemma":[0.000499084,0.00008379462,0.00351884,0.001796597,0.00001562092,0.00000506981,0.00002046457,0.1591652,0.006256744,0.001681976,0.8266449,0.0003117186],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.02667515,0.8523015,0.04613153,0.04777866,0.002833671,0.005761531,0.00001967949,0.0005554256,0.01794286],"genre_scores_gemma":[0.8418761,0.1478955,0.0005084216,0.001886417,0.0003863945,0.003205258,0.000002996657,0.00003933225,0.00419949],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.9551706,"threshold_uncertainty_score":0.9978073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09652963668837547,"score_gpt":0.3908739109914948,"score_spread":0.2943442743031194,"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."}}