{"id":"W2091362266","doi":"10.1145/1557626.1557658","title":"Texture map","year":2009,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Artificial intelligence; Image texture; Pattern recognition (psychology); Image segmentation; Computer science; Computer vision; Texture filtering; Scale-space segmentation; Segmentation; Texture compression; Segmentation-based object categorization; Preprocessor; Robustness (evolution); Wavelet transform; Mathematics; Wavelet","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005008718,0.00003434908,0.00003233858,0.00002248902,0.00002890186,0.00006179145,0.0003350615,0.00002258429,0.00004212246],"category_scores_gemma":[0.000004620479,0.0000248613,0.00001956605,0.0001296249,0.000006266167,0.0001856718,0.00001897754,0.00003720264,0.0001799037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006643758,"about_ca_system_score_gemma":0.000009673688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.23722e-7,"about_ca_topic_score_gemma":6.447694e-8,"domain_scores_codex":[0.9996818,0.000006416839,0.00005253471,0.0001050644,0.00008113847,0.00007298972],"domain_scores_gemma":[0.9997001,0.000005339478,0.00001325542,0.0002267638,0.00002836804,0.00002621395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[2.906365e-7,0.00001594699,0.000007079992,5.626887e-7,4.32732e-7,0.000001390917,0.00002074914,1.852053e-8,0.004526086,0.5989899,0.01370117,0.3827364],"study_design_scores_gemma":[0.0001253377,0.000158723,0.007443138,0.000007035894,0.000001397497,0.00001511524,0.000009405474,0.01026612,0.3555273,0.1923139,0.4338845,0.0002480974],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00001332739,0.00004395795,0.9341858,0.01197401,0.00003336829,0.00003100927,6.33683e-8,0.000492578,0.0532259],"genre_scores_gemma":[0.8248485,0.00001038199,0.1516519,0.004908794,0.00004328532,0.000001832725,6.028556e-7,0.000001535319,0.01853319],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8248352,"threshold_uncertainty_score":0.2312358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009136986218230458,"score_gpt":0.2441387872314223,"score_spread":0.2350018010131918,"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."}}