{"id":"W4294176560","doi":"10.4038/sljastats.v23i1.8058","title":"ptsuite: Fast Tail Index Estimation for Power Law Distributions in R","year":2022,"lang":"en","type":"article","venue":"Sri Lankan Journal of Applied Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"R package; Pareto distribution; Code (set theory); Computer science; Index (typography); Pareto principle; Heuristic; Estimation; Power law; Power (physics); Algorithm; Data mining; Statistics; Mathematics; Set (abstract data type); Computational science; Programming language; Engineering; Artificial intelligence","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":[0.001057973,0.0001533797,0.0003919496,0.0001194827,0.0002229912,0.00005084058,0.0002423955,0.00005397765,0.0004509504],"category_scores_gemma":[0.0007954572,0.0001443083,0.00005811267,0.0002291439,0.00009602404,0.00005146816,0.00006462016,0.0004903481,0.000002485191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001759501,"about_ca_system_score_gemma":0.000143215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006576738,"about_ca_topic_score_gemma":0.00001769802,"domain_scores_codex":[0.9982271,0.00009403332,0.0007827203,0.0001469667,0.0004646813,0.0002845222],"domain_scores_gemma":[0.997107,0.001915592,0.0005024076,0.0001637077,0.0001984354,0.0001128995],"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":[0.0001908722,0.0001825939,0.00005884746,0.00005615797,0.00002848179,0.00003084548,0.0005813169,0.0001817439,0.0001075901,0.9821809,0.004298049,0.0121026],"study_design_scores_gemma":[0.001093275,0.0003458088,0.0002539087,0.00002146124,0.00005701078,0.00003946931,0.0007168198,0.003602465,0.000113525,0.9893023,0.004284821,0.0001691463],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007136661,0.00001126912,0.9904402,0.0001035025,0.0002212157,0.0002949002,0.003580538,0.00001255104,0.004622179],"genre_scores_gemma":[0.3868302,0.000001232722,0.6129689,0.00006277119,0.00003418755,0.00003637716,0.00004437493,0.000009231798,0.00001274386],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3861165,"threshold_uncertainty_score":0.5884722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02979063462805738,"score_gpt":0.3462156382777814,"score_spread":0.316425003649724,"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."}}