{"id":"W2966624573","doi":"","title":"GEOMATICS: A KEY COMPONENT OF MANAGING THE GUARANI AQUIFER SYSTEM","year":2019,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Hydropower, Displacement, Environmental Impact","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geomatics; Aquifer; Component (thermodynamics); Geography; Key (lock); Groundwater; Cartography; Water resource management; Hydrology (agriculture); Geology; Environmental science; Computer science; Geotechnical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001581007,0.000569362,0.0004168263,0.00182075,0.00222351,0.004548748,0.001837281,0.0009702385,0.006432815],"category_scores_gemma":[0.003282676,0.0002137958,0.0001433904,0.002429649,0.0009666079,0.002699955,0.0034843,0.001282004,0.001247289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003076952,"about_ca_system_score_gemma":0.009180482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02038318,"about_ca_topic_score_gemma":0.02916016,"domain_scores_codex":[0.9990362,0.000248691,0.00004761389,0.000108156,0.0002671229,0.000292222],"domain_scores_gemma":[0.9979809,0.0001416966,0.000356813,0.0001293832,0.0006675383,0.0007237929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00007836665,0.0003157544,0.04352971,0.0007981016,0.0001298474,0.001120358,0.005608994,0.02751241,0.009947442,0.0281217,0.08322088,0.7996164],"study_design_scores_gemma":[0.00003731903,0.0003773441,0.08677392,0.0007757353,0.0001260693,0.0004750627,0.01937286,0.02570266,0.005603869,0.02586081,0.8347895,0.0001048907],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3380087,0.01448273,0.1139758,0.1398953,0.001159009,0.003326916,0.002874692,0.007787508,0.3784894],"genre_scores_gemma":[0.887014,0.008179079,0.07794809,0.003147025,0.0003948272,0.0004683339,0.001264628,0.0004454046,0.02113855],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02038318,"threshold_uncertainty_score":0.04052907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01241458142975714,"score_gpt":0.3348624487576808,"score_spread":0.3224478673279237,"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."}}