{"id":"W4401816701","doi":"10.1007/s10853-024-10108-6","title":"A comprehensive analysis of human cranial morphology: multiscale characterization and statistical analysis","year":2024,"lang":"en","type":"article","venue":"Journal of Materials Science","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Defence Research and Development Canada; University of Alberta","funders":"","keywords":"Characterization (materials science); Materials science; Solid mechanics; Morphology (biology); Statistical analysis; Morphological analysis; Nanotechnology; Computer science; Composite material; Biology; Artificial intelligence; Mathematics; Statistics","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":[0.000510111,0.0004229909,0.0004706496,0.002016247,0.0002992675,0.0005249993,0.0002499424,0.0002472367,0.001079277],"category_scores_gemma":[0.001305046,0.0001665786,0.0007180304,0.001352619,0.0002778476,0.0004763353,0.0006232579,0.0002886697,0.0002764993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001530881,"about_ca_system_score_gemma":0.0005197773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001349957,"about_ca_topic_score_gemma":0.001977549,"domain_scores_codex":[0.9998013,0.00003319565,0.00001412812,0.00005201464,0.0000839105,0.00001539892],"domain_scores_gemma":[0.9995708,0.0001043173,0.00007089531,0.0001089393,0.0001162715,0.00002870199],"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":[0.0001658512,0.0001063915,0.03168192,0.0002727215,0.0003401565,0.0003405974,0.0002256503,0.06758801,0.2284738,0.01030759,0.003925276,0.6565722],"study_design_scores_gemma":[0.00001676537,0.0002732544,0.298717,0.00006137492,0.0003233993,0.001067156,0.0002198709,0.6501294,0.02342261,0.01893142,0.006745595,0.00009210157],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5000792,0.001211893,0.4933636,0.0002540514,0.00002816389,0.00006980196,0.002050026,0.0007327096,0.002210747],"genre_scores_gemma":[0.8758096,0.0006722878,0.1209117,0.00003722051,0.00005450963,0.00005871355,0.001547356,0.0001530181,0.0007555744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002016247,"threshold_uncertainty_score":0.003610611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04412331486196732,"score_gpt":0.3512046024697987,"score_spread":0.3070812876078314,"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."}}