{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004435401,0.002492904,0.00181579,0.002284383,0.0007209122,0.003520838,0.005506678,0.002142377,0.182631],"category_scores_gemma":[0.01645918,0.002088422,0.002887557,0.002813936,0.0007155737,0.003218428,0.003409812,0.003936553,0.1312783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007736355,"about_ca_system_score_gemma":0.001901951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006562141,"about_ca_topic_score_gemma":0.01175112,"domain_scores_codex":[0.9983525,0.0006824953,0.0001174681,0.0003319464,0.0004532982,0.0000622341],"domain_scores_gemma":[0.9944775,0.00347476,0.0001514422,0.001092303,0.0006749762,0.0001291506],"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":[0.00007808884,0.0001083884,0.0006428797,0.0009450954,0.0002978433,0.0001771528,0.0001596777,0.02854348,0.001181133,0.05962125,0.6246775,0.2835676],"study_design_scores_gemma":[0.0001154374,0.00006185831,0.0009543255,0.0005830166,0.0001112224,0.0003657663,0.00007668773,0.155811,0.002387483,0.2629142,0.5764346,0.0001843899],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002163721,0.0009427142,0.9288946,0.0003989903,0.0003781688,0.000132567,0.01316718,0.04770891,0.008160558],"genre_scores_gemma":[0.00683081,0.001933741,0.9106774,0.0006462053,0.0004955275,0.001034072,0.02221207,0.04137503,0.0147951],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.182631,"threshold_uncertainty_score":0.6109616,"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."}}