{"id":"W4382072741","doi":"10.59934/jaiea.v1i3.97","title":"Knearst Algorithm Analysis – Neighbor Breast Cancer Prediction Coimbra","year":2022,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence and Engineering Applications (JAIEA)","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Algorithm; Breast cancer; k-nearest neighbors algorithm; Regression analysis; Python (programming language); Linear regression; Computer science; Simple linear regression; Regression; Artificial intelligence; Mathematics; Statistics; Machine learning; Cancer; Medicine; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003875499,0.00011235,0.0001880778,0.0004924661,0.000282956,0.0001247964,0.0004387795,0.00003691291,0.0001437099],"category_scores_gemma":[0.000007758408,0.0001188158,0.0001189975,0.001879817,0.00002984856,0.0003625257,0.0001189439,0.0002877756,0.000006115668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002120719,"about_ca_system_score_gemma":0.00007851661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004524777,"about_ca_topic_score_gemma":0.000005369695,"domain_scores_codex":[0.9987691,0.0000246475,0.0004704659,0.0002207292,0.0003437282,0.0001713452],"domain_scores_gemma":[0.9991658,0.00006214566,0.0002388761,0.0002423669,0.0001879771,0.0001027914],"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":[0.00001131944,0.00007258483,0.0001153072,0.000008843864,0.0002051129,0.00000313743,0.000236326,0.535774,0.00158528,0.003582595,0.0001944927,0.458211],"study_design_scores_gemma":[0.00002626054,0.0000998776,0.001752745,0.000007234454,0.0001381223,0.0001378962,0.0001368142,0.98397,0.001603926,0.0007777428,0.0112136,0.0001358073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00316207,0.0003815647,0.994935,0.0009084565,0.0003649809,0.0001106407,0.00005228198,0.00006317921,0.00002179318],"genre_scores_gemma":[0.9633732,0.0003066481,0.03532078,0.00006811183,0.0006293888,0.0001993097,0.000004993291,0.00001816001,0.00007933898],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9602112,"threshold_uncertainty_score":0.4845169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01510694524122819,"score_gpt":0.2494985985237779,"score_spread":0.2343916532825497,"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."}}