{"id":"W6926440158","doi":"10.25318/3810009501-eng","title":"Median, minimum and maximum monthly water yield for selected ecoprovinces","year":2019,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Microbial Natural Products and Biosynthesis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Subdivision; Yield (engineering); Hydrology (agriculture); Land use; Vegetation (pathology); Macro","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001251558,0.0002724083,0.0004186477,0.0001053923,0.00009123683,0.00004496625,0.0001034837,0.0002348822,0.0002846179],"category_scores_gemma":[0.001136631,0.0001927575,0.00002164152,0.00008542496,0.00004337154,0.00003730505,0.00003419407,0.0002195753,0.000004963582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001749373,"about_ca_system_score_gemma":0.0004950927,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04349357,"about_ca_topic_score_gemma":0.4598683,"domain_scores_codex":[0.9986066,0.00001683514,0.0003193936,0.0004245954,0.0003168218,0.0003157522],"domain_scores_gemma":[0.9986275,0.0003898033,0.0001422024,0.0002548846,0.0004564469,0.0001290918],"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":[0.0001034559,0.00002466103,0.000004269399,0.001339203,0.00006891083,0.00002505746,0.00001481508,1.553364e-7,0.004895246,0.000003790045,0.9902044,0.003316039],"study_design_scores_gemma":[0.000261316,0.0002162505,0.0004551099,0.0002846888,0.0004559434,0.00001290446,0.00004249822,0.00006078357,0.01880031,0.00006051454,0.9790049,0.0003447944],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005984039,0.0002088191,0.0000372012,0.001810402,0.0007156837,0.0009408341,0.9956694,0.000009225944,0.00001003334],"genre_scores_gemma":[0.00288545,0.0001335165,0.0008013328,0.0002665158,0.000263549,0.000008980509,0.9936965,0.00002572648,0.001918494],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4163747,"threshold_uncertainty_score":0.9628759,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008742274037348388,"score_gpt":0.2417662563575378,"score_spread":0.2330239823201894,"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."}}