{"id":"W4398309434","doi":"10.7910/dvn/g8q7zg/npkyko","title":"Historical_Trend_MAT_Precip_CMD.tab","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Sustainable Agricultural Systems Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Replication (statistics); Productivity; Geography; Climate change; Physical geography; Forestry; Environmental science; Climatology; Agroforestry; Ecology; Geology; Biology; Economics","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":[0.000234418,0.0004075408,0.0005402417,0.00007615062,0.0001221982,0.00008908137,0.001097329,0.0003242542,0.2677795],"category_scores_gemma":[0.0001569362,0.0003091298,0.0002479113,0.0004480774,0.00008824711,0.0004185486,0.001306462,0.000377936,0.7199699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001364375,"about_ca_system_score_gemma":0.00002232513,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009253586,"about_ca_topic_score_gemma":0.0005024979,"domain_scores_codex":[0.9974384,0.0001210041,0.0003978556,0.0007981061,0.0007323271,0.0005122949],"domain_scores_gemma":[0.9977764,0.0000797566,0.0002816423,0.001625039,0.00001756535,0.0002196217],"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":[0.000007390171,0.00005621343,0.0000531926,0.00006131523,0.000060856,0.00008769618,0.000007346664,0.0001124504,0.00001706032,0.00000242153,0.9993228,0.0002112672],"study_design_scores_gemma":[0.0001722684,0.00002743262,0.000200058,0.00002293569,0.000220272,0.00001397181,0.00005178141,0.00001331985,0.000002887177,0.0000104905,0.998808,0.0004566004],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003077343,0.000002072939,0.00001086478,0.000007627631,0.0008657555,0.0003061345,0.995266,0.00004582355,0.00346495],"genre_scores_gemma":[0.00002608792,0.000116896,0.00008621758,0.0002119006,0.0002940761,0.0000395252,0.977967,0.00001732076,0.02124099],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4521904,"threshold_uncertainty_score":0.9999361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01208772274389992,"score_gpt":0.2157761193809878,"score_spread":0.2036883966370879,"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."}}