{"id":"W4390461978","doi":"10.3929/ethz-b-000073064","title":"Nonparametric Econometrics: The np Package","year":2008,"lang":"en","type":"article","venue":"Repository for Publications and Research Data (ETH Zurich)","topic":"Data Analysis with R","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Nonparametric statistics; Econometrics; R package; Statistics; Computer science; Mathematics; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.004146518,0.0001298693,0.0001849706,0.001453263,0.002244633,0.001340081,0.005859527,0.00007934398,0.000009022984],"category_scores_gemma":[0.002807213,0.00008792341,0.00006005731,0.006427352,0.0004276276,0.002688684,0.002542713,0.0003632879,0.00005477193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000728939,"about_ca_system_score_gemma":0.0003199253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002023396,"about_ca_topic_score_gemma":0.00001221443,"domain_scores_codex":[0.9970993,0.0003406441,0.00038783,0.000948131,0.0006955863,0.0005285217],"domain_scores_gemma":[0.9910557,0.002412356,0.0001451007,0.00538289,0.0007388844,0.0002650216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006040974,0.0003357528,0.00924242,0.00003936634,0.0001622211,0.000009134049,0.000276209,0.000007189775,0.000175771,0.08390609,0.8814934,0.02434635],"study_design_scores_gemma":[0.0002237309,0.00008695037,0.01654216,0.000003949126,0.0000165622,0.0001724188,0.00004868601,0.03703764,0.0002269502,0.0008905353,0.9445742,0.000176238],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009928709,0.006413895,0.9274317,0.03150224,0.0004963799,0.00223574,0.0008278509,0.0003911628,0.0207723],"genre_scores_gemma":[0.8597273,0.00320217,0.1066395,0.0005046216,0.0007474129,0.0008975854,0.002011106,0.00006340969,0.02620681],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8497986,"threshold_uncertainty_score":0.9996966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2633315052726449,"score_gpt":0.38542908937566,"score_spread":0.1220975841030151,"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."}}