{"id":"W4393462991","doi":"10.5281/zenodo.3735533","title":"SCDNA: a serially complete precipitation and temperature dataset in North America from 1979 to 2018 (Version 1.1)","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Environmental DNA in Biodiversity Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; University of Saskatchewan","funders":"","keywords":"Precipitation; Climatology; Environmental science; Meteorology; Geography; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009054314,0.0008648401,0.0008071235,0.002429793,0.0006001645,0.001031675,0.001653047,0.0006075436,0.01480454],"category_scores_gemma":[0.00327833,0.0005905351,0.0007878446,0.005088634,0.0002389667,0.001188142,0.00129084,0.00134056,0.01072148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001120225,"about_ca_system_score_gemma":0.003430419,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1214377,"about_ca_topic_score_gemma":0.179708,"domain_scores_codex":[0.9991721,0.0001178004,0.0001331167,0.000263568,0.0002170838,0.00009630097],"domain_scores_gemma":[0.9974348,0.0002203755,0.0004441116,0.000423284,0.00131313,0.0001643441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00009153996,0.000060225,0.02662306,0.0004807946,0.0001474688,0.00007207145,0.0002211207,0.001169435,0.0004707013,0.0008861512,0.9603779,0.009399647],"study_design_scores_gemma":[0.0001961782,0.00002412172,0.1317802,0.0002517533,0.00006406488,0.0000652228,0.0003932953,0.002494264,0.0008285659,0.0008917758,0.8629411,0.00006939415],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003131729,0.0000606944,0.0005538254,0.0001038476,0.00005672613,0.00003651503,0.9943194,0.0005923801,0.001144856],"genre_scores_gemma":[0.004827943,0.00004992769,0.002328948,0.00004825913,0.0000254204,0.0001925234,0.9916648,0.0001302818,0.0007318052],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8785623,"threshold_uncertainty_score":0.2414617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02302314221468381,"score_gpt":0.2124557735340963,"score_spread":0.1894326313194125,"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."}}