{"id":"W6948375714","doi":"10.5063/f1fx77p9","title":"Mines in Alaska with subsetting by watershed and SASAP region, 2010 to 2016","year":2018,"lang":"en","type":"dataset","venue":"UC Santa Barbara","topic":"Photovoltaic Systems and Sustainability","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Watershed; Footprint; Hydrology (agriculture); Surface mining; Water resources; Commission","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":[0.0005298945,0.0009697616,0.0007548799,0.003168668,0.0005895703,0.001080261,0.0009155536,0.0006555648,0.00876667],"category_scores_gemma":[0.002439974,0.0004158625,0.0008875955,0.005755778,0.0002534288,0.0009205508,0.001073221,0.0008707212,0.008480972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008990107,"about_ca_system_score_gemma":0.00167864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09223951,"about_ca_topic_score_gemma":0.1470319,"domain_scores_codex":[0.9994111,0.00005173268,0.0001067976,0.0002093826,0.0001417245,0.00007920775],"domain_scores_gemma":[0.9990269,0.0001138897,0.000218647,0.0001655714,0.0003755749,0.00009937813],"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":[0.0003030768,0.0001091748,0.127477,0.001494262,0.0003134437,0.0002409813,0.000350456,0.003076112,0.0004745174,0.001150829,0.8518013,0.01320887],"study_design_scores_gemma":[0.0001638293,0.00003649868,0.213131,0.0007730607,0.0001478119,0.0002344006,0.001154768,0.00153238,0.0007442376,0.0008761177,0.7811399,0.00006592979],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.004644135,0.0001552643,0.00006859733,0.00003978777,0.00002588294,0.0000101608,0.9939212,0.0001247089,0.001010265],"genre_scores_gemma":[0.00505137,0.0001297672,0.0002784323,0.00001961653,0.000009385467,0.0000554166,0.9936641,0.00002271967,0.0007690969],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09223951,"threshold_uncertainty_score":0.1834052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005762247341060732,"score_gpt":0.2103568399061608,"score_spread":0.2045945925651001,"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."}}