{"id":"W2461502243","doi":"10.1016/b978-012077790-7/50004-7","title":"Fundamental Enhancement Techniques","year":2000,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003203253,0.0007398393,0.0006172542,0.0003439493,0.0001740336,0.000286694,0.001986125,0.0003771548,0.001061949],"category_scores_gemma":[0.000003318137,0.0007488143,0.0003093359,0.00002816804,0.0001827022,0.0002805733,0.0007297266,0.0006184345,0.001020561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003547275,"about_ca_system_score_gemma":0.0001564547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.101432e-7,"about_ca_topic_score_gemma":0.000004066405,"domain_scores_codex":[0.9968221,0.00003358813,0.0007067528,0.001068866,0.0007962971,0.0005724356],"domain_scores_gemma":[0.9976412,0.00003163806,0.0003400328,0.001745135,0.0000922545,0.0001497151],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004622877,0.00001841843,2.383949e-7,0.0000282183,0.00004780009,0.00005517361,0.00008496694,8.912282e-9,0.0006433169,0.03522808,0.001605178,0.962284],"study_design_scores_gemma":[0.00009418119,0.0002139731,6.083894e-7,0.0004365581,0.0000260687,0.00002364384,5.968007e-7,0.00001045423,0.0470892,0.0500551,0.9013296,0.0007199652],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.000002110418,0.0006583847,0.01371013,0.0001203171,0.000320101,0.001164117,0.00001103938,0.001532057,0.9824817],"genre_scores_gemma":[0.0002284274,0.0004779622,0.07293782,0.001262029,0.0002806369,0.0002405335,0.00002106277,0.00009647619,0.924455],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.961564,"threshold_uncertainty_score":0.9998512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01224040686638845,"score_gpt":0.2439509916156011,"score_spread":0.2317105847492127,"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."}}