{"id":"W2767397540","doi":"10.14288/1.0340342","title":"Detecting and imaging time-lapse conductivity changes using electromagnetic methods","year":2017,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Remote sensing; Computer science; Geology","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":[0.000364592,0.00006398687,0.000258971,0.0000555536,0.0005299841,0.0002030966,0.0002990371,0.00006968666,0.00002287741],"category_scores_gemma":[0.0001461608,0.0002370897,0.00004209334,0.00008229241,0.0003575015,0.0004623995,0.0002047976,0.0001844189,0.000001389001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008977167,"about_ca_system_score_gemma":0.00001879864,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02881307,"about_ca_topic_score_gemma":0.01903389,"domain_scores_codex":[0.9992087,0.00006669936,0.0000735252,0.0002829906,0.00009734683,0.0002707183],"domain_scores_gemma":[0.9992768,0.00008036002,0.0001144806,0.000361393,0.00008752528,0.00007941225],"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":[0.00000181508,0.000009641591,0.009688626,0.00009312643,0.00002678139,0.00006389257,0.00008082591,0.000001496058,0.3868142,0.000001636679,0.0000482795,0.6031697],"study_design_scores_gemma":[0.0003973674,0.00005666077,0.9774343,0.0002277134,0.00006924515,0.0003906321,0.0001541074,0.0133214,0.0005636991,0.007031964,0.000009398756,0.0003435218],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901533,0.0001926995,0.007172366,0.00001751701,0.00007360043,0.0001562212,0.00001619492,0.000428607,0.001789477],"genre_scores_gemma":[0.7044612,0.00002563989,0.2954546,0.000002733328,0.00001842236,2.854625e-7,6.850182e-7,0.00002221317,0.00001428313],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9677457,"threshold_uncertainty_score":0.9988662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01665105309006757,"score_gpt":0.232206836796842,"score_spread":0.2155557837067744,"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."}}