{"id":"W2566311797","doi":"10.1109/eusipco.2016.7760621","title":"Automatic pigment identification on roman Egyptian paintings by using sparse modeling of hyperspectral images","year":2016,"lang":"en","type":"article","venue":"","topic":"Cultural Heritage Materials Analysis","field":"Arts and Humanities","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Palette (painting); Hyperspectral imaging; Painting; Pigment; Reflectivity; Hematite; Identification (biology); Artificial intelligence; Inpainting; Ground truth; Computer science; Pattern recognition (psychology); Geology; Mineralogy; Image (mathematics); Art; Optics; Art history; Physics; Visual arts","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":[0.0005266088,0.0005404297,0.0003812281,0.0009234397,0.0002670084,0.0006351564,0.0004563398,0.0005081962,0.000460515],"category_scores_gemma":[0.0009918243,0.0002613821,0.0005606822,0.0005402355,0.0004571507,0.0005404748,0.0004196381,0.0003931398,0.0003022351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003145594,"about_ca_system_score_gemma":0.0002910452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002352152,"about_ca_topic_score_gemma":0.003780079,"domain_scores_codex":[0.9997718,0.00004905396,0.000007545531,0.00007466266,0.00006294773,0.00003403038],"domain_scores_gemma":[0.9997439,0.00009284271,0.0000431422,0.00005493859,0.00005442499,0.00001070731],"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":[0.000376167,0.0001487485,0.006800674,0.0001987968,0.0001185341,0.0004580286,0.0003774366,0.4035989,0.1942217,0.002691172,0.001588652,0.3894211],"study_design_scores_gemma":[0.000004229689,0.00001747762,0.002544871,0.000004475066,0.00001160992,0.0000611257,0.00004782831,0.9792756,0.01699819,0.0006377391,0.0003885662,0.000008328628],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.491321,0.0002838533,0.5053363,0.0002282496,0.00002587716,0.00004281124,0.0001389405,0.0007407009,0.001882264],"genre_scores_gemma":[0.7949973,0.0002838882,0.2019493,0.00005189045,0.00002991977,0.00002354687,0.0003537095,0.00007885622,0.002231471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002352152,"threshold_uncertainty_score":0.004676998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04678101101432686,"score_gpt":0.240539727969506,"score_spread":0.1937587169551791,"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."}}