{"id":"W2884802339","doi":"10.7287/peerj.preprints.26952v1","title":"Innovation in graduate training: a skills-focused graduate program in fisheries science","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Data Analysis with R","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Training (meteorology); Government (linguistics); Work (physics); Class (philosophy); Graduate students; Graduate education; Fisheries science; Space (punctuation); Computer science; Fishery; Mathematics education; Political science; Business; Sociology; Engineering; Geography; Psychology; Fisheries management; Fishing; Pedagogy; Meteorology; Artificial intelligence","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004228919,0.0004748159,0.0006754306,0.003270902,0.000124604,0.001597831,0.004724881,0.0002008581,0.00001716094],"category_scores_gemma":[0.0009156521,0.0004341319,0.00008809525,0.01176285,0.0008129476,0.001856256,0.003684369,0.0009414477,0.00005739694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003332769,"about_ca_system_score_gemma":0.00134618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009395499,"about_ca_topic_score_gemma":0.001554428,"domain_scores_codex":[0.9942539,0.0001568778,0.001307941,0.001932667,0.001423554,0.0009250603],"domain_scores_gemma":[0.9963881,0.00007853044,0.0005569188,0.002161913,0.0007018544,0.00011273],"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":[0.00002328689,0.001412942,0.02546076,0.0001825098,0.00007065501,0.000154565,0.02502135,0.0004792034,0.0002524545,0.04155028,0.003998711,0.9013933],"study_design_scores_gemma":[0.001905714,0.0004728987,0.3764963,0.0009319693,0.0000331479,0.00001847827,0.0005304518,0.4398272,0.003842852,0.1707608,0.002535914,0.00264421],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8935997,0.00001313119,0.09140439,0.004166813,0.001153882,0.001536606,0.0000126381,0.0007974311,0.007315334],"genre_scores_gemma":[0.8028495,0.00001442643,0.1961337,0.0003090245,0.00006944028,0.0003248863,0.00006791965,0.00002124737,0.0002098701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8987491,"threshold_uncertainty_score":0.9998111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.139384331261379,"score_gpt":0.3365980390283789,"score_spread":0.1972137077669999,"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."}}