{"id":"W6929491990","doi":"10.48448/g9s3-sv64","title":"Nonparametric Decoding for Generative Retrieval","year":2023,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Decoding methods; Nonparametric statistics; Generative grammar; Generative model; Parametric statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001792602,0.001506182,0.001642907,0.001172571,0.0005942154,0.002088411,0.002302365,0.001715567,0.006920551],"category_scores_gemma":[0.01276351,0.0005105,0.001214692,0.001755849,0.001451414,0.004234186,0.003060594,0.002205029,0.005254894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001086482,"about_ca_system_score_gemma":0.001709269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005391158,"about_ca_topic_score_gemma":0.005528504,"domain_scores_codex":[0.9981779,0.0007453258,0.0001335208,0.0004606885,0.0003376418,0.0001448658],"domain_scores_gemma":[0.9966109,0.001986146,0.0001424793,0.0008342955,0.000347726,0.00007835523],"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":[0.0003115107,0.0001656384,0.001236117,0.0006054021,0.0001407347,0.0002975279,0.0003419253,0.243207,0.0126386,0.118347,0.01983379,0.6028747],"study_design_scores_gemma":[0.00002880162,0.00006172171,0.0001932865,0.00003617748,0.00002581239,0.0002272019,0.00004863224,0.8813423,0.004778777,0.1080063,0.00520633,0.00004465841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.005118834,0.0008141451,0.9887399,0.0003601488,0.00006972108,0.00006530947,0.0005732338,0.001430129,0.002828638],"genre_scores_gemma":[0.5336134,0.002269964,0.4357304,0.001322497,0.0004915302,0.0006868325,0.007366582,0.001263542,0.01725515],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.006920551,"threshold_uncertainty_score":0.02315152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06157298538011681,"score_gpt":0.3582033227596573,"score_spread":0.2966303373795405,"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."}}