{"id":"W2981329419","doi":"10.4095/299796","title":"Improving the spatial density of a regional hydraulic conductivity dataset with estimates made from domestic water well information","year":2017,"lang":"en","type":"report","venue":"","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hydraulic conductivity; Environmental science; Spatial analysis; Conductivity; Data mining; Hydrology (agriculture); Soil science; Computer science; Geography; Geology; Remote sensing; Geotechnical engineering; Chemistry","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":[0.00348096,0.0006981735,0.0008799898,0.004551914,0.0006083959,0.001984173,0.001627073,0.0007644974,0.001283585],"category_scores_gemma":[0.02206251,0.0004170806,0.0009047471,0.007249399,0.0007500469,0.002003148,0.003035442,0.0007421906,0.001194247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001494678,"about_ca_system_score_gemma":0.002909751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1280989,"about_ca_topic_score_gemma":0.1761974,"domain_scores_codex":[0.9974778,0.0005048324,0.0003798551,0.0008086742,0.0006432641,0.000185443],"domain_scores_gemma":[0.9868425,0.002904184,0.00134367,0.004001256,0.004752809,0.0001556114],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003931847,0.0002570873,0.5351738,0.001331724,0.0007716249,0.0006553262,0.002183593,0.08525724,0.01820211,0.007040592,0.02637733,0.3223563],"study_design_scores_gemma":[0.0001740881,0.0001396862,0.6439812,0.0005561174,0.0005072608,0.0004864152,0.002664755,0.206761,0.02016575,0.01075453,0.1135147,0.0002944891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.559742,0.002120318,0.3232683,0.001755953,0.0002376617,0.0004929533,0.0825597,0.007929151,0.02189397],"genre_scores_gemma":[0.7981681,0.0006042453,0.1287932,0.0002555892,0.00008745769,0.0002848363,0.06962238,0.0003751543,0.00180905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1280989,"threshold_uncertainty_score":0.2547065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03120951374188176,"score_gpt":0.2834781718271888,"score_spread":0.2522686580853071,"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."}}