{"id":"W2937143200","doi":"10.1109/ecai.2018.8678935","title":"The Performances of the Fixed Constraints Transform Applied in Text Compression Experimental Results and Comparisons","year":2018,"lang":"en","type":"article","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação para a Ciência e a Tecnologia","keywords":"Lossless compression; Computer science; Compression (physics); Data compression; Word (group theory); Phrase; Compression ratio; Data compression ratio; Search engine indexing; Algorithm; Binary number; Image compression; Encoding (memory); Speech recognition; Arithmetic; Artificial intelligence; Mathematics; Image (mathematics); Image processing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001142881,0.000979027,0.0006886281,0.001909152,0.0004815776,0.0009206284,0.0008597333,0.0007962791,0.003508858],"category_scores_gemma":[0.005959529,0.0001661797,0.0004016332,0.002643833,0.0005217348,0.001050423,0.000548503,0.000400056,0.001051335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005622194,"about_ca_system_score_gemma":0.0004987419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004439401,"about_ca_topic_score_gemma":0.00240739,"domain_scores_codex":[0.9984208,0.0002259409,0.0001831303,0.000226458,0.0007600159,0.0001835617],"domain_scores_gemma":[0.9973801,0.001274494,0.0001356083,0.0002626433,0.000870965,0.00007621876],"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":[0.005338265,0.0006914862,0.004755294,0.001452944,0.000198354,0.0006659036,0.0005469008,0.1055004,0.2294782,0.00181619,0.004167743,0.6453884],"study_design_scores_gemma":[0.0001891137,0.00290539,0.01060538,0.00008683535,0.0001814147,0.000641412,0.0004708975,0.3374686,0.639773,0.000829523,0.006752584,0.00009576727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9071118,0.004065081,0.06838841,0.000243697,0.0002321781,0.0002698454,0.00156593,0.005483859,0.01263918],"genre_scores_gemma":[0.9097006,0.001646373,0.07816956,0.00007394783,0.00006756633,0.0001758102,0.00407306,0.0005074528,0.005585544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004439401,"threshold_uncertainty_score":0.0117383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01534526417115194,"score_gpt":0.2561478140732352,"score_spread":0.2408025499020833,"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."}}