{"id":"W2041520574","doi":"10.1159/000022955","title":"Ascertainment and Anticipation in Family Studies","year":2000,"lang":"en","type":"article","venue":"Human Heredity","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"National Cancer Institute","keywords":"Anticipation (artificial intelligence); Genetics; Biology; Family studies; Evolutionary biology; Psychology; Computer science; Artificial intelligence","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":[0.03846695,0.0003260927,0.0007564501,0.001719226,0.001000523,0.001071745,0.00052882,0.0006366179,0.001127475],"category_scores_gemma":[0.1714496,0.0004596688,0.0005566346,0.002078578,0.001610643,0.001533331,0.00158224,0.0006734129,0.00007801627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007441534,"about_ca_system_score_gemma":0.0009699714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004921821,"about_ca_topic_score_gemma":0.005194535,"domain_scores_codex":[0.9584759,0.03668615,0.00104973,0.002178787,0.001244608,0.0003648387],"domain_scores_gemma":[0.8624173,0.1152651,0.01210271,0.008395921,0.001008263,0.0008107603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00088844,0.00005075776,0.8982872,0.0003447781,0.0009928051,0.001498864,0.002490894,0.02258186,0.001055578,0.02479357,0.0007617078,0.04625361],"study_design_scores_gemma":[0.0002672248,0.001212941,0.7835655,0.0005509739,0.001439134,0.005174415,0.001296886,0.08253098,0.002174063,0.1107316,0.01091544,0.0001407854],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8895833,0.007296251,0.09594291,0.001105128,0.0001472033,0.0002826599,0.0008766936,0.0001027398,0.00466311],"genre_scores_gemma":[0.9899933,0.0007659009,0.008690527,0.00007162958,0.00003968344,0.0001171602,0.0001558024,0.000006799371,0.000159275],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03846695,"threshold_uncertainty_score":0.2034351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08826232376475761,"score_gpt":0.3731676979251463,"score_spread":0.2849053741603887,"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."}}