{"id":"W2957756763","doi":"10.2134/csa2019.64.0702","title":"Agricultural Water Quality in Cold Environments","year":2019,"lang":"en","type":"article","venue":"CSA News","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Snowmelt; Cold climate; Agriculture; Cold winter; Joke; Cold storage; Meteorology; Environmental science; Snow; Geography; Archaeology","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":[0.001147358,0.0003142,0.0002764535,0.0005285413,0.003399952,0.003026079,0.0004255411,0.0009200072,0.02156715],"category_scores_gemma":[0.001734724,0.0002137131,0.0003071474,0.001271908,0.0006054764,0.001354793,0.002011975,0.001759172,0.003993027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002533836,"about_ca_system_score_gemma":0.003097178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04533621,"about_ca_topic_score_gemma":0.1172821,"domain_scores_codex":[0.9993458,0.0001300442,0.00003249088,0.0001148267,0.0002363903,0.0001404161],"domain_scores_gemma":[0.9983926,0.0001385371,0.000221156,0.00007928914,0.0007570626,0.0004112558],"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":[0.00009997017,0.00005285369,0.01433099,0.0005009069,0.00003035804,0.0003518947,0.002521673,0.0001079242,0.002256623,0.001748078,0.8232049,0.1547939],"study_design_scores_gemma":[0.000008901999,0.00006051702,0.05077165,0.0003074117,0.00002064942,0.0001828375,0.00451847,0.00006432703,0.0007989745,0.001026635,0.942214,0.00002578538],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.09670368,0.1230812,0.002989808,0.2921518,0.03925466,0.0002247237,0.005330003,0.0007919751,0.4394721],"genre_scores_gemma":[0.4505647,0.1051899,0.003519637,0.05300336,0.0137806,0.000284591,0.005191298,0.0005042781,0.3679617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04533621,"threshold_uncertainty_score":0.09014463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008814600480588226,"score_gpt":0.2115978230678793,"score_spread":0.2027832225872911,"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."}}