{"id":"W6957829986","doi":"10.6073/pasta/114f9cad8cba211f1cd0a4136ea66543","title":"MODIS annual maximum NDVI for Tanana-Yukon Uplands Ecoregion from 2000-2012","year":2018,"lang":"en","type":"dataset","venue":"Environmental Data Initiative","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Normalized Difference Vegetation Index; Ecoregion; Taiga; Pixel; Moderate-resolution imaging spectroradiometer; Boreal; Vegetation Index","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003340763,0.001025191,0.0005998059,0.001924473,0.0005357739,0.0008215079,0.001410055,0.0005760624,0.01436136],"category_scores_gemma":[0.00103141,0.0003607315,0.0005090342,0.004334322,0.000229344,0.0006752956,0.0007089035,0.0006920838,0.0146865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001417096,"about_ca_system_score_gemma":0.002773054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.167207,"about_ca_topic_score_gemma":0.2852598,"domain_scores_codex":[0.9997084,0.00001973909,0.00003192003,0.00008478492,0.00009200578,0.00006310627],"domain_scores_gemma":[0.9994037,0.00003537552,0.00005229047,0.0001121578,0.000333732,0.00006273653],"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":[0.0001052357,0.00005627272,0.007569571,0.0004167873,0.00005798226,0.00007120128,0.00007634566,0.0009226878,0.0003637693,0.0004611479,0.9833729,0.0065262],"study_design_scores_gemma":[0.0001295881,0.0000198085,0.05825531,0.0002067726,0.00004824115,0.00009234628,0.0003473537,0.001687973,0.0009327133,0.0005482119,0.9376897,0.00004205787],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001133651,0.00003022015,0.00005637823,0.00002404168,0.00001466733,0.0000122291,0.9979278,0.0001334569,0.0006675005],"genre_scores_gemma":[0.001248632,0.00002057562,0.0001458904,0.000006685456,0.00000176254,0.00003391711,0.9979412,0.00002065193,0.0005807298],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.167207,"threshold_uncertainty_score":0.3324675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05139487013727639,"score_gpt":0.2801685998140261,"score_spread":0.2287737296767497,"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."}}