{"id":"W4409787565","doi":"10.61091/jcmcc127a-269","title":"Multiscale processing of image gradient domain-based convolutional neural networks for visual feature enhancement","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Feature (linguistics); Computer science; Artificial intelligence; Image (mathematics); Pattern recognition (psychology); Domain (mathematical analysis); Image enhancement; Computer vision; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002297478,0.0003639738,0.0002848901,0.0003487827,0.0000865636,0.0002138982,0.0003479611,0.0002531634,0.0006381642],"category_scores_gemma":[0.0004313742,0.0001131752,0.0004188911,0.0003376404,0.0001728136,0.0004386651,0.0002553507,0.0003244526,0.0001375964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003307642,"about_ca_system_score_gemma":0.0002289535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002498996,"about_ca_topic_score_gemma":0.003636832,"domain_scores_codex":[0.9999204,0.00001089414,0.000003989615,0.00001825384,0.00003559179,0.00001095419],"domain_scores_gemma":[0.9999238,0.00001655026,0.00001211475,0.00001230811,0.00003016522,0.00000495114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001535227,0.00008868779,0.001395765,0.0001729902,0.0001071885,0.0001866844,0.00007051769,0.4045346,0.1989854,0.01334381,0.002279024,0.3786817],"study_design_scores_gemma":[0.00000247307,0.00002496733,0.0004760339,0.000003020199,0.00001428331,0.00003763288,0.000002944249,0.9858947,0.01169544,0.001032526,0.000811555,0.000004487313],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05951594,0.0007255125,0.9369255,0.0001299329,0.00005534295,0.00003208082,0.00006088404,0.000503756,0.00205097],"genre_scores_gemma":[0.7323723,0.000760578,0.2632305,0.0001017572,0.00003601225,0.00004111422,0.0001423663,0.00007039111,0.003245055],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002498996,"threshold_uncertainty_score":0.004968882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00670934210747141,"score_gpt":0.2642149744258086,"score_spread":0.2575056323183372,"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."}}