{"id":"W2478396075","doi":"10.48550/arxiv.1607.06235","title":"Haze Visibility Enhancement: A Survey and Quantitative Benchmarking","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Visibility; Benchmark (surveying); Benchmarking; Haze; Ground truth; Computer science; Image (mathematics); Image enhancement; Computer vision; Artificial intelligence; Remote sensing; Optics; Geography; Physics; Cartography","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"],"consensus_categories":[],"category_scores_codex":[0.0011383,0.0003871829,0.0004069463,0.0002582557,0.000173153,0.0001936025,0.001559813,0.0002230101,0.00004987315],"category_scores_gemma":[0.0001271615,0.0003930247,0.0001119825,0.00041333,0.0002402573,0.0007671638,0.003693305,0.0003966776,0.00004138749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002780854,"about_ca_system_score_gemma":0.0001687871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002448226,"about_ca_topic_score_gemma":0.0001337677,"domain_scores_codex":[0.9969782,0.0004765908,0.0002761136,0.001692713,0.0001383375,0.000438019],"domain_scores_gemma":[0.9974612,0.000308397,0.0003570353,0.001443014,0.0002932916,0.0001370837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003344817,0.0008988399,0.06573913,0.0006717855,0.000684409,0.000624535,0.002207813,0.0005941137,0.004787693,0.8958501,0.004117959,0.02348918],"study_design_scores_gemma":[0.001979447,0.001132661,0.0693095,0.001346669,0.0001265833,0.000007816481,0.00007374198,0.4081631,0.02415611,0.4890068,0.001139661,0.00355785],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1761326,0.0000874447,0.8204392,0.00006515199,0.0003512186,0.0004065896,0.00002682574,0.0002854441,0.00220558],"genre_scores_gemma":[0.9802169,0.000282148,0.01842145,0.00006313166,0.00003304111,0.000003141695,0.00001642241,0.00001600128,0.000947802],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8040842,"threshold_uncertainty_score":0.9998522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09210372285924155,"score_gpt":0.2331432360318526,"score_spread":0.141039513172611,"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."}}