{"id":"W4224298359","doi":"10.1016/j.jhydrol.2022.127809","title":"Evaluation of slim-hole NMR logging for hydrogeologic insights into dolostone and sandstone aquifers","year":2022,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada; University of Guelph","funders":"Office of Energy Research and Development; Canada's Michael Smith Genome Sciences Centre; University of Guelph","keywords":"Geology; Hydrogeology; Borehole; Lithology; Well logging; Dolostone; Aquifer; Porosity; Petrophysics; Bedrock; Mineralogy; Geomorphology; Sedimentary rock; Petrology; Geophysics; Geotechnical engineering; Groundwater; Carbonate rock; Geochemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001534248,0.0003528326,0.0002260273,0.0008207514,0.0003828946,0.0006403957,0.0006976444,0.00030128,0.0005029943],"category_scores_gemma":[0.004188407,0.0002140022,0.00009610269,0.0007468737,0.0005470779,0.0007285602,0.0004471694,0.0001352627,0.0001606722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000908096,"about_ca_system_score_gemma":0.001091039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04258631,"about_ca_topic_score_gemma":0.1802889,"domain_scores_codex":[0.9992855,0.0001468213,0.00003788492,0.0001305501,0.0003063791,0.00009285202],"domain_scores_gemma":[0.9971954,0.0008783858,0.0004358382,0.0002346185,0.001090846,0.0001650711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001104262,0.0002685046,0.6565146,0.0002160771,0.00003489383,0.0003504178,0.001645755,0.005863259,0.1805083,0.0001721349,0.000295517,0.1530262],"study_design_scores_gemma":[0.00005685113,0.001791555,0.8575618,0.00006769499,0.00008273874,0.0005009305,0.003285666,0.04939476,0.08355975,0.0002069715,0.003426011,0.00006521559],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947882,0.00007717631,0.00415327,0.00002841935,0.00000382094,0.0000565155,0.0002076298,0.00009335735,0.0005916001],"genre_scores_gemma":[0.9867688,0.0001014837,0.01252836,0.0000241418,0.000005072273,0.00003983098,0.0001985333,0.00001506741,0.0003187087],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04258631,"threshold_uncertainty_score":0.08467686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01902607786842677,"score_gpt":0.3454921037805433,"score_spread":0.3264660259121166,"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."}}