{"id":"W4393625629","doi":"10.5281/zenodo.3735534","title":"SCDNA: a serially complete precipitation and temperature dataset in North America from 1979 to 2018","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; Geography; Meteorology; 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":[0.0006440823,0.0007643743,0.0007096619,0.001942765,0.0005812091,0.0007684599,0.001530225,0.0005553536,0.01155142],"category_scores_gemma":[0.002558193,0.0003852252,0.0005807569,0.004251774,0.0002193586,0.0008169046,0.001093585,0.0009678589,0.008907775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001128889,"about_ca_system_score_gemma":0.002802866,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1200746,"about_ca_topic_score_gemma":0.171636,"domain_scores_codex":[0.9993374,0.00009150821,0.00009548946,0.00022903,0.0001629797,0.00008352705],"domain_scores_gemma":[0.9979221,0.000189342,0.0003506951,0.000353562,0.001018881,0.0001653757],"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":[0.0001083411,0.00005866607,0.02335223,0.0003443337,0.00009564947,0.00007881888,0.000157567,0.001174379,0.0004109404,0.0006856391,0.9654984,0.008035011],"study_design_scores_gemma":[0.0002137012,0.0000247076,0.1514885,0.0002476347,0.00005099939,0.00007406143,0.0004100256,0.002597878,0.0008751421,0.00076354,0.8431859,0.00006793413],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003354095,0.00005554068,0.0002493252,0.00009649668,0.00004061711,0.00002114812,0.994989,0.0002727735,0.0009209921],"genre_scores_gemma":[0.004411349,0.00004677906,0.001155497,0.00004591339,0.00002383046,0.0001515571,0.9934912,0.00005890996,0.0006148228],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8799254,"threshold_uncertainty_score":0.2387514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02635590077690501,"score_gpt":0.2165335544153739,"score_spread":0.1901776536384689,"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."}}