{"id":"W2890895565","doi":"10.1016/j.enggeo.2018.09.011","title":"Integrated GPR and ERT data interpretation for bedrock identification at Cléricy, Québec, Canada","year":2018,"lang":"en","type":"article","venue":"Engineering Geology","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; SNC-Lavalin (Canada); Polytechnique Montréal; Université du Québec en Abitibi-Témiscamingue","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ground-penetrating radar; Bedrock; Geology; Electrical resistivity tomography; Borehole; Lithology; Excavation; Geotechnical investigation; Geophysical survey; Geotechnical engineering; Geophysics; Radar; Geomorphology; Seismology; Mining engineering; Remote sensing; Petrology; Electrical resistivity and conductivity; Engineering","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":[0.0004816237,0.000420275,0.0002839007,0.001220321,0.00196543,0.001205532,0.0009398561,0.0005318396,0.007688543],"category_scores_gemma":[0.0008184228,0.0002383464,0.0002074961,0.001456926,0.0003462584,0.0005320964,0.0004450262,0.0005326541,0.002102712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01034384,"about_ca_system_score_gemma":0.02297715,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9871846,"about_ca_topic_score_gemma":0.9952109,"domain_scores_codex":[0.9995525,0.00002940069,0.00001063192,0.00007121372,0.0001953281,0.0001409584],"domain_scores_gemma":[0.9988072,0.00004340389,0.00002441145,0.00002509922,0.001010954,0.0000889517],"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":[0.001111131,0.0004988067,0.2700618,0.0004247904,0.0001531939,0.001858653,0.002578769,0.03183274,0.08637376,0.003045914,0.1430158,0.4590447],"study_design_scores_gemma":[0.0001424128,0.00009742569,0.8059655,0.0001481193,0.000078326,0.0002777163,0.003587956,0.09163149,0.01303091,0.0003842421,0.08452027,0.0001357479],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8355897,0.001193584,0.03374281,0.00279392,0.000231333,0.0006563864,0.03524914,0.004306435,0.08623667],"genre_scores_gemma":[0.8800206,0.0003923005,0.02868829,0.0004195381,0.00003304023,0.0001111173,0.008422409,0.0004363492,0.08147632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01281542,"threshold_uncertainty_score":0.07505012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01238689205511846,"score_gpt":0.2384436018934381,"score_spread":0.2260567098383197,"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."}}