{"id":"W1481045648","doi":"10.1002/9781444342116.app1","title":"Appendix 1: Growth and BMI Charts","year":2011,"lang":"en","type":"other","venue":"","topic":"Williams Syndrome Research","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Growth chart; Chart; Medicine; Achondroplasia; Pediatrics; Statistics; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001046282,0.0009598786,0.000640803,0.003416893,0.0007001034,0.0008639103,0.001008841,0.0005227632,0.5929276],"category_scores_gemma":[0.011197,0.0004133067,0.0004851743,0.003550243,0.0001880133,0.0009676763,0.0008271001,0.001307517,0.3511759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001091791,"about_ca_system_score_gemma":0.002233411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02012536,"about_ca_topic_score_gemma":0.02350297,"domain_scores_codex":[0.9993926,0.00009666003,0.00007037923,0.00005473629,0.000325385,0.00006020401],"domain_scores_gemma":[0.9924189,0.002195475,0.0004221174,0.0005545774,0.003861032,0.0005479347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004570574,0.00005204042,0.0008855067,0.00007098729,0.000001784605,0.00003249706,0.00001633393,0.0001051125,0.00004606245,0.0005818288,0.9719355,0.02622664],"study_design_scores_gemma":[0.0000668719,0.00004608367,0.01479718,0.0003068399,0.000005688868,0.0003345144,0.0001093175,0.0002669608,0.0002741379,0.001961237,0.9817984,0.00003285565],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002877494,0.0005765209,0.004850713,0.001212252,0.0009422607,0.001469598,0.652443,0.005471244,0.3301569],"genre_scores_gemma":[0.01170857,0.00243704,0.02629098,0.001335213,0.0004369336,0.004199374,0.5955829,0.004093722,0.3539152],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5929276,"threshold_uncertainty_score":0.5806394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05558939088377629,"score_gpt":0.28485177209086,"score_spread":0.2292623812070837,"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."}}