{"id":"W7125956533","doi":"10.1145/3757377.3794993","title":"10.1145/3757377.3794993","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Session (web analytics); Representation (politics); Gaussian; Component (thermodynamics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002147064,0.002801804,0.002157378,0.002672559,0.001871813,0.004135816,0.00277489,0.004694699,0.9095992],"category_scores_gemma":[0.004407342,0.001612859,0.001237361,0.009437079,0.001632766,0.007947966,0.004122433,0.002880739,0.9318123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00252619,"about_ca_system_score_gemma":0.001388937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03131287,"about_ca_topic_score_gemma":0.02531292,"domain_scores_codex":[0.9992586,0.00006862126,0.00006219382,0.0002175121,0.0002700518,0.0001230343],"domain_scores_gemma":[0.9983614,0.0004611768,0.00007296655,0.0005736106,0.0003533576,0.000177448],"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":[0.0002562269,0.0001557616,0.0005640781,0.0004654619,0.00005028167,0.0001760004,0.00006820456,0.001576785,0.000801425,0.01004676,0.6327991,0.3530399],"study_design_scores_gemma":[0.00004385394,0.00002591806,0.0008412392,0.0002898819,0.00005295559,0.0001331757,0.00006825295,0.002688731,0.0005604241,0.005179043,0.9900828,0.00003374197],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.002760676,0.008806249,0.03494937,0.002908473,0.002770218,0.0005315635,0.03000604,0.0210843,0.8961831],"genre_scores_gemma":[0.007707634,0.004405806,0.0060305,0.001079097,0.0001969492,0.0002502855,0.01321243,0.002418801,0.9646985],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.09040076,"threshold_uncertainty_score":0.1289457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004478300184377838,"score_gpt":0.169664695066428,"score_spread":0.1651863948820502,"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."}}