{"id":"W1553997149","doi":"","title":"Discrete Shearlet Transform : New Multiscale Directional Image Representation","year":2009,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Shearlet; Computer science; Representation (politics); Computer vision; Artificial intelligence; Image (mathematics); Computer graphics (images)","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005234183,0.0004466901,0.0004912051,0.0002940256,0.0004748132,0.001349611,0.002330304,0.0003250879,0.0001006269],"category_scores_gemma":[0.00102344,0.0004729345,0.0004106912,0.0006356993,0.0001922717,0.0007674321,0.001024976,0.000902004,0.00006304601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001622601,"about_ca_system_score_gemma":0.0005048417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002495682,"about_ca_topic_score_gemma":0.0005102689,"domain_scores_codex":[0.9910486,0.005683649,0.0006986327,0.001285355,0.0007846404,0.0004990681],"domain_scores_gemma":[0.9940416,0.001288312,0.0004173874,0.002590499,0.001330183,0.000331992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004406171,0.0005667681,0.0002590842,0.0001273251,0.0001233833,0.00004386095,0.01247407,0.0003470855,0.0193691,0.07032041,0.009356895,0.886968],"study_design_scores_gemma":[0.003513709,0.000003559399,0.01822905,0.002867714,0.0001497291,0.0001303749,0.00007912346,0.2242108,0.5004029,0.2194803,0.02866757,0.002265163],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001944544,0.0005393982,0.907221,0.02329449,0.0004155453,0.0004629406,0.0000235077,0.0005031019,0.06559549],"genre_scores_gemma":[0.05883424,0.0003455231,0.9227802,0.0002558974,0.0001035569,0.00004249366,0.0002928965,0.00004590704,0.01729927],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8847028,"threshold_uncertainty_score":0.9997723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0188725147567287,"score_gpt":0.276475350594291,"score_spread":0.2576028358375623,"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."}}