{"id":"W2792860027","doi":"10.5220/0006616901430149","title":"Low Complex Image Resizing Algorithm using Fixed-point Integer Transformation","year":2018,"lang":"en","type":"article","venue":"","topic":"Image and Video Stabilization","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Integer (computer science); Resizing; Transformation (genetics); Computer science; Image (mathematics); Algorithm; Point (geometry); Artificial intelligence; Computer vision; Mathematics; Geometry","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":[0.0002876771,0.0001063988,0.000102568,0.000107835,0.0002126289,0.0002535809,0.0003142323,0.00003854946,0.0001956612],"category_scores_gemma":[0.00002369472,0.00009309596,0.00004459674,0.0003562462,0.00006931252,0.002153996,0.00007857219,0.00006351285,0.0001329246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005756314,"about_ca_system_score_gemma":0.0000404528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006693105,"about_ca_topic_score_gemma":0.00002055923,"domain_scores_codex":[0.9990529,0.00005200656,0.000248803,0.0002264322,0.0001913266,0.00022854],"domain_scores_gemma":[0.9993121,0.00002817107,0.00005470406,0.0003082132,0.0002462536,0.00005057693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001519098,0.0001728083,0.00004125193,0.00008620843,0.00002527241,0.00001339241,0.01500621,0.000088764,0.3182289,0.03289276,0.005772996,0.6276562],"study_design_scores_gemma":[0.0001897163,0.00005255045,0.0001351502,0.00002158257,0.000003076689,0.00001500687,0.000129127,0.8437862,0.1533118,0.001247811,0.0009748827,0.0001330543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002246206,0.000004948943,0.9878662,0.0005139026,0.0002081526,0.0001433376,0.000001381568,0.0001940602,0.008821823],"genre_scores_gemma":[0.3476838,0.000002410889,0.6513091,0.0007333317,0.0001556341,0.00000273187,0.00000771194,0.000009213373,0.00009603405],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8436975,"threshold_uncertainty_score":0.3796343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03184003272809908,"score_gpt":0.2910713000078832,"score_spread":0.2592312672797841,"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."}}