{"id":"W2294493990","doi":"10.1007/978-3-319-20801-5_51","title":"Color Space Identification for Image Display","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Color histogram; Computer vision; Color space; Artificial intelligence; RGB color model; Computer science; Color image; RGB color space; Color balance; False color; Color quantization; Color depth; ICC profile; Computer graphics (images); Color model; Image (mathematics); Image processing","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":[0.0004621544,0.0002067686,0.0002067323,0.0001700716,0.0002236353,0.0002639972,0.0008080407,0.00006975815,0.00005036446],"category_scores_gemma":[0.0000169631,0.0001875529,0.00008250572,0.0002389245,0.0004443001,0.0002443808,0.0001874163,0.0001908074,0.00007647793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009986348,"about_ca_system_score_gemma":0.0003476352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000246745,"about_ca_topic_score_gemma":0.00002661461,"domain_scores_codex":[0.998445,0.000005126522,0.0002409726,0.000680243,0.0003322755,0.0002963822],"domain_scores_gemma":[0.9987605,0.0001638009,0.0001857819,0.0005189746,0.0002691108,0.0001018382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001282612,0.00009093258,0.0002101742,0.00002865829,0.00001649241,0.000002076807,0.0007394707,0.01225514,0.005233812,0.4437987,0.001569244,0.5360425],"study_design_scores_gemma":[0.0003278374,0.00009586973,0.0001728826,0.00008510172,0.00002656428,0.000002482663,0.000001790841,0.2115178,0.0044553,0.7456202,0.03709996,0.0005942378],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005356853,0.00003450541,0.9920335,0.0009020323,0.0004378697,0.0006149505,0.00007321032,0.00002582013,0.005342471],"genre_scores_gemma":[0.8612329,0.000002943004,0.1303265,0.000231666,0.001561515,0.0002170161,0.0001332056,0.00004431536,0.006249909],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8617069,"threshold_uncertainty_score":0.7648184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01942900937850258,"score_gpt":0.2890780565055603,"score_spread":0.2696490471270577,"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."}}