{"id":"W2549823869","doi":"","title":"ENV-638: ADVANCED TECHNIQUES FOR SITE CHARACTERIZATION: REAL-TIME HIGH-RESOLUTION SITE CHARACTERIZATION OF THE SUBSURFACE USING MIP, LIF AND HPT","year":2016,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Engineering Applied Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Characterization (materials science); Remote sensing; Mineralogy; Geology; Environmental science; Chemistry; Materials science; Nanotechnology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002609664,0.0002842696,0.0003011604,0.0003120909,0.0001645792,0.00008014428,0.000351552,0.0002259753,0.000008131638],"category_scores_gemma":[0.00002872701,0.0002492534,0.00007846484,0.0004452975,0.0001028009,0.001180844,0.0001927235,0.0002121404,0.00001821885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002534359,"about_ca_system_score_gemma":0.00003491888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002125302,"about_ca_topic_score_gemma":0.00002346935,"domain_scores_codex":[0.9985865,0.00008876985,0.0002756942,0.0003670729,0.0002785382,0.0004034072],"domain_scores_gemma":[0.9989955,0.00009540016,0.0001478453,0.0004685668,0.0001677401,0.0001249382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005985859,0.00001565511,0.0908948,0.0001500876,0.00003176972,0.000003384711,0.000113946,0.0003131063,0.9073297,0.00003153107,9.584146e-8,0.001056114],"study_design_scores_gemma":[0.0007163625,0.00005852203,0.5926905,0.0004578542,0.00005226223,0.000008042243,0.000007524049,0.0002141467,0.4046642,0.00001045446,0.00077451,0.0003455855],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9628574,0.00001426449,0.03555081,0.00008054472,0.000139374,0.0006821583,0.0002833757,0.0003785705,0.00001354615],"genre_scores_gemma":[0.9977015,0.0002466451,0.0006210839,0.0000119927,0.00008092999,0.00000629372,0.0001092643,0.00008790264,0.001134336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5026654,"threshold_uncertainty_score":0.9999959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03256943458356244,"score_gpt":0.2642668312835583,"score_spread":0.2316973966999959,"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."}}