{"id":"W33459561","doi":"10.1007/978-3-642-31900-6_40","title":"A Granular Computing Perspective on Image Organization within an Image Retrieval Context","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina; Memorial University of Newfoundland","funders":"","keywords":"Granular computing; Computer science; Image retrieval; Abstraction; Context (archaeology); Perspective (graphical); Aggregate (composite); Image (mathematics); Cluster analysis; Information retrieval; Domain (mathematical analysis); Image processing; Field (mathematics); Automatic image annotation; Artificial intelligence; Data mining; Mathematics; 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":[0.001502882,0.0007575722,0.00153614,0.004687421,0.001032579,0.006472075,0.002010157,0.001739778,0.003682954],"category_scores_gemma":[0.004410405,0.0007463173,0.001265047,0.006965451,0.003810935,0.01116024,0.002691815,0.002241855,0.0005052687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001577875,"about_ca_system_score_gemma":0.0004910331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002235945,"about_ca_topic_score_gemma":0.001789068,"domain_scores_codex":[0.9985275,0.0003435885,0.0001685655,0.0002275031,0.0005592592,0.0001735314],"domain_scores_gemma":[0.9982014,0.0007347748,0.0003129329,0.0003192139,0.0002308294,0.0002007681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004198401,0.00002606257,0.0003235858,0.0002115721,0.00004116443,0.0002506087,0.0003528617,0.009670474,0.002505237,0.9649286,0.001473162,0.0201745],"study_design_scores_gemma":[0.00001171395,0.00003555245,0.000707079,0.00006698387,0.00005519552,0.0001952314,0.0002497683,0.04727975,0.0009127198,0.9428908,0.007562353,0.00003285882],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02741805,0.008945044,0.9342448,0.004393341,0.0004904715,0.00008523365,0.0002882131,0.0003201351,0.02381466],"genre_scores_gemma":[0.5989767,0.006981139,0.3834333,0.0006022446,0.00147533,0.0001588107,0.0003017986,0.0001574162,0.007913198],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006472075,"threshold_uncertainty_score":0.01232064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01537200521224091,"score_gpt":0.2495699276667907,"score_spread":0.2341979224545498,"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."}}