{"id":"W4385434633","doi":"10.1007/978-3-031-38299-4_60","title":"Using a Riemannian Elastic Metric for Statistical Analysis of Tumor Cell Shape Heterogeneity","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Metric (unit); Computer science; Representation (politics); Pattern recognition (psychology); Statistical analysis; Artificial intelligence; Information geometry; Mathematics; Statistics; Geometry","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":[0.00153554,0.0006981861,0.0007704283,0.001415969,0.0003322861,0.001289032,0.0009097094,0.0009375392,0.001097679],"category_scores_gemma":[0.003726383,0.0004569028,0.001073015,0.001459985,0.0007680231,0.001394667,0.001352252,0.0009502477,0.0004586608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006392895,"about_ca_system_score_gemma":0.0004555224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001875213,"about_ca_topic_score_gemma":0.002173037,"domain_scores_codex":[0.9995683,0.000141284,0.00003709529,0.00008248973,0.0001371212,0.00003372457],"domain_scores_gemma":[0.9984739,0.0008051092,0.0002024847,0.000235448,0.0002081687,0.00007489807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001004501,0.0000846056,0.003894555,0.0003128043,0.0003068217,0.0004179912,0.0002480042,0.4335932,0.07840967,0.2422685,0.005320658,0.2350427],"study_design_scores_gemma":[0.000001584558,0.00002170494,0.001295135,0.000006474359,0.00001516906,0.0001064519,0.00001466059,0.9576369,0.002536163,0.03673065,0.001611362,0.00002379551],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01446526,0.00035453,0.9839453,0.0001216963,0.00003692318,0.0000173225,0.00009177011,0.0001654834,0.0008016741],"genre_scores_gemma":[0.4657441,0.001714123,0.5212749,0.0003039286,0.000283115,0.0001783385,0.0009417429,0.0007555294,0.008804259],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001875213,"threshold_uncertainty_score":0.008120775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08679973668701545,"score_gpt":0.3352811828866585,"score_spread":0.248481446199643,"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."}}