{"id":"W2991549798","doi":"10.1101/852772","title":"Kappa ( <i>κ</i> ): Analysis of Curvature in Biological Image Data using B-splines","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Curvature; Initialization; Pixel; Algorithm; Computer science; Artificial intelligence; Radius of curvature; Mathematics; Computer vision; Geometry; Mean curvature","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009501306,0.0005425301,0.001111122,0.0006687733,0.00003753963,0.00008593188,0.001695457,0.00102187,0.00002574217],"category_scores_gemma":[0.000397214,0.0005395033,0.0003865851,0.001378211,0.0001970619,0.00001855406,0.002945723,0.0005780142,0.00000469147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006506456,"about_ca_system_score_gemma":0.0003487638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000182405,"about_ca_topic_score_gemma":0.00003072692,"domain_scores_codex":[0.9965832,0.0002115564,0.0008106822,0.001683973,0.0002733946,0.0004371472],"domain_scores_gemma":[0.9944422,0.00003321694,0.0006644572,0.004279536,0.0004736925,0.0001069373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000351008,0.0001533488,0.05647865,0.0001120951,0.001180672,0.00001767587,0.000001317435,0.0003818767,0.9411682,0.000007117374,0.0004628522,0.00000108586],"study_design_scores_gemma":[0.000305368,0.00005158686,0.04367211,0.000141674,0.001703795,1.45545e-8,0.000002505469,0.009184245,0.9404253,8.507326e-7,0.003677233,0.0008352606],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9861286,0.002945498,0.00962429,0.00002922847,0.000100669,0.0004583135,0.0006351459,0.00006531745,0.00001292904],"genre_scores_gemma":[0.9787863,0.001210126,0.01949833,0.0001335617,0.0002118825,0.00002565321,0.00005576442,0.00007452526,0.000003834953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01280653,"threshold_uncertainty_score":0.9997057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02557235112286203,"score_gpt":0.2808730750562435,"score_spread":0.2553007239333815,"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."}}