{"id":"W6931215511","doi":"10.5281/zenodo.2604627","title":"Hierarchical Generalized Additive Models: an introduction with mgcv","year":2019,"lang":"en","type":"other","venue":"Figshare","topic":"Experimental Learning in Engineering","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina; Fisheries and Oceans Canada","funders":"","keywords":"Metadata; Set (abstract data type); Term (time); Feature (linguistics)","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00001205716,0.000336298,0.0002709446,0.0001705567,0.00001720741,0.00004075847,0.0001952479,0.0002731909,0.1427764],"category_scores_gemma":[0.00002103093,0.0003273999,0.00004514147,0.0001080607,0.000006024763,0.0001291654,0.00003768569,0.0004742981,0.001916969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001022739,"about_ca_system_score_gemma":0.00001767222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005984104,"about_ca_topic_score_gemma":0.000006886329,"domain_scores_codex":[0.999064,0.00002094726,0.0001106483,0.0003436985,0.0001956591,0.0002650494],"domain_scores_gemma":[0.9994239,0.00001477229,0.00003880453,0.0004122628,0.00001837962,0.0000918747],"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":[0.000004385782,0.000007533174,7.95756e-8,0.0001620541,0.00006376729,0.00000688689,0.00006477921,0.183431,0.00009475222,0.00007707736,0.8155639,0.0005237715],"study_design_scores_gemma":[0.0002203765,0.00005123734,0.000001961366,0.0008035927,0.000008285388,0.00001224097,0.00001251399,0.1057102,0.0003404126,0.000003292035,0.8924239,0.0004119385],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"dataset","genre_scores_codex":[0.00001964771,0.001866444,0.0005092713,0.00002521347,0.00071055,0.0008952672,0.1684832,0.005047035,0.8224433],"genre_scores_gemma":[0.001355381,0.00003926939,0.01369109,0.0000499047,0.006510324,0.0009267703,0.5613492,0.003949854,0.4121282],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.4103152,"threshold_uncertainty_score":0.9999178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01413260520124027,"score_gpt":0.2145216863830047,"score_spread":0.2003890811817645,"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."}}