{"id":"W6931145076","doi":"10.5281/zenodo.3775781","title":"Preserving the agricultural data story at the Ontario Agricultural College","year":2018,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Computability, Logic, AI Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Agriculture; RDM; Variety (cybernetics); Christian ministry; Social research; Presentation (obstetrics); Research council; Applied research","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.001596906,0.0001935746,0.0001406271,0.00005296612,0.006621337,0.001430489,0.009377856,0.00005449784,0.004091979],"category_scores_gemma":[0.000556296,0.0001043176,0.00005825922,0.0008358263,0.000445429,0.0008487027,0.02009409,0.0004571491,0.004846411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005375308,"about_ca_system_score_gemma":0.0000135359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004108449,"about_ca_topic_score_gemma":0.0005482131,"domain_scores_codex":[0.996979,0.0008431241,0.0002474634,0.0007619341,0.0006761833,0.000492272],"domain_scores_gemma":[0.996507,0.0001342606,0.0001348903,0.002303001,0.0007772264,0.0001436137],"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":[0.00001601116,0.00007941848,0.00001355776,0.000012633,0.00005388703,0.000007162904,0.00817457,0.00005495775,0.001279676,0.007383451,0.9630464,0.01987826],"study_design_scores_gemma":[0.000223767,0.0001492937,0.02840924,0.0000102134,0.0000107583,0.0002445888,0.0006236665,0.003188563,0.0001866087,0.0002192848,0.9665425,0.0001914797],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3777507,0.0006468782,0.05642504,0.05761243,0.00316283,0.004822441,0.0008797364,0.005041948,0.493658],"genre_scores_gemma":[0.9709803,0.00001913764,0.00335366,0.0007343867,0.001356536,2.742348e-7,0.001212521,0.0005625099,0.02178068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5932297,"threshold_uncertainty_score":0.9996061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05800143300932929,"score_gpt":0.2410828996120538,"score_spread":0.1830814666027245,"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."}}