{"id":"W4415360450","doi":"10.59934/jaiea.v5i1.1680","title":"The Application of A Priori Algorithms in Determining the Relationship Between Maternal Age and Pregnancy Conditions","year":2025,"lang":"","type":"article","venue":"Journal of Artificial Intelligence and Engineering Applications (JAIEA)","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Pregnancy; Confidence interval; Advanced maternal age; Hypertension in Pregnancy; Maternal health; Affect (linguistics); Health data","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":[0.0007682025,0.0002331045,0.000351692,0.0003100036,0.000278364,0.0001496858,0.0003599556,0.0001722866,0.000001080521],"category_scores_gemma":[0.0005434593,0.0001805477,0.00009731903,0.0007420194,0.0002350042,0.0001417132,0.00007537695,0.0006745079,0.00000173657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008961524,"about_ca_system_score_gemma":0.0000601234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002706261,"about_ca_topic_score_gemma":0.00002051936,"domain_scores_codex":[0.9979724,0.00003586629,0.001315404,0.0002030233,0.0001987881,0.0002745678],"domain_scores_gemma":[0.9961657,0.002923814,0.0002761112,0.0003517407,0.0001922156,0.00009043723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0000169311,0.0001781148,0.1745016,0.001150826,0.0002630114,0.000006198135,0.001290325,0.291455,0.001445319,0.157052,0.00001741032,0.3726233],"study_design_scores_gemma":[0.0001406119,0.00009934873,0.7785369,0.001627055,0.0002513809,0.00001016476,0.0003500168,0.1935977,0.002168259,0.02088291,0.002027978,0.0003076151],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3730024,0.006800621,0.6185052,0.0004940761,0.0003359911,0.0007550988,0.00003236808,0.00002668336,0.00004755646],"genre_scores_gemma":[0.9949535,0.002233344,0.002524062,0.000004089565,0.0001375341,0.0001082186,0.000003974082,0.00002303187,0.00001224894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6219511,"threshold_uncertainty_score":0.7362523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02832166741494069,"score_gpt":0.2990561991490953,"score_spread":0.2707345317341546,"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."}}