{"id":"W1593431850","doi":"10.1023/a:1011141618114","title":"Complexity, Confusion, and Perceptual Grouping. Part II: Mapping Complexity","year":2001,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Espace pour la vie","funders":"","keywords":"Element (criminal law); Texture (cosmology); Tangent; Context (archaeology); Mathematics; Computation; Artificial intelligence; Orientation (vector space); Enhanced Data Rates for GSM Evolution; Curse of dimensionality; Computer science; Image (mathematics); Pattern recognition (psychology); Geometry; Algorithm; Geography","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":[0.001207155,0.0005964325,0.0006819941,0.00177543,0.0005495182,0.00330313,0.000804918,0.0008125195,0.006585204],"category_scores_gemma":[0.01760273,0.0003979459,0.0007654757,0.001712456,0.003192407,0.005919548,0.001919967,0.001463701,0.0005867321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009628554,"about_ca_system_score_gemma":0.0005113644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001521073,"about_ca_topic_score_gemma":0.0007265626,"domain_scores_codex":[0.9990954,0.0002312463,0.00008428805,0.0001521377,0.0003184422,0.0001183931],"domain_scores_gemma":[0.9911035,0.006564758,0.0007505388,0.0006611594,0.0006347548,0.0002852643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001077051,0.0003339544,0.02201887,0.00125039,0.0003768087,0.0004428926,0.003211818,0.03999843,0.02902204,0.3946178,0.01603393,0.4916161],"study_design_scores_gemma":[0.00003750661,0.0003143644,0.05660212,0.0002053251,0.0001232859,0.0007605926,0.001032432,0.06821183,0.005791796,0.8552076,0.01156957,0.0001435504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3703876,0.05807863,0.5071259,0.00559999,0.001552767,0.0003818869,0.0004961787,0.000490047,0.05588702],"genre_scores_gemma":[0.9570833,0.006619829,0.02887274,0.0002426722,0.001092121,0.0002100391,0.0003421163,0.000110094,0.005427134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006585204,"threshold_uncertainty_score":0.0220297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.061013875360454,"score_gpt":0.3365137670450444,"score_spread":0.2754998916845904,"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."}}