{"id":"W1993078811","doi":"10.3758/brm.42.3.809","title":"Automatic detection and quantification of growth spurts","year":2010,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Infant Health and Development","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Growth spurt; Computer science; Statistics; Mathematics; 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":[0.01560345,0.00008647108,0.0002040608,0.0003834232,0.0007941279,0.000009404919,0.0001376311,0.0002389404,0.0004391184],"category_scores_gemma":[0.002678087,0.00007410431,0.00002490068,0.0004389042,0.0001724884,0.00009427094,0.0001170379,0.001358542,0.00006334705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006254522,"about_ca_system_score_gemma":0.0004874217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007560895,"about_ca_topic_score_gemma":0.0002157404,"domain_scores_codex":[0.9954316,0.002773289,0.0005604735,0.0002508578,0.0004453285,0.0005384704],"domain_scores_gemma":[0.9968758,0.001674081,0.0001496483,0.0003438168,0.0007184765,0.000238242],"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":[0.00003496725,0.00007755707,0.05197509,0.0005217189,0.000003377587,0.000002154381,0.001898512,1.08787e-8,0.4665251,0.002642673,0.0001999494,0.4761188],"study_design_scores_gemma":[0.0004083486,0.0001030926,0.9289102,0.00007104387,0.0000103222,0.000006184964,0.000960251,0.0004009128,0.06200578,0.001566052,0.005448612,0.0001092177],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987803,0.00004275034,0.007902667,0.0002864779,0.0007509167,0.001483,0.000005252657,0.000064776,0.001661111],"genre_scores_gemma":[0.8276562,0.00004511182,0.17105,0.00003462087,0.00008374422,0.0007197376,0.000005063272,0.00001796826,0.0003875935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8769351,"threshold_uncertainty_score":0.610787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4191227010303741,"score_gpt":0.6821904125655601,"score_spread":0.263067711535186,"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."}}