{"id":"W3196071263","doi":"10.1109/aim46487.2021.9517696","title":"Automatic Material Classification via Proprioceptive Sensing and Wavelet Analysis During Excavation","year":2021,"lang":"en","type":"article","venue":"","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"NCR","keywords":"Wavelet; Excavation; Computer science; Artificial intelligence; Pattern recognition (psychology); Geology; Geotechnical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004582115,0.00007316931,0.0001234751,0.00005327037,0.00006126814,0.00005119929,0.00001957684,0.00003583861,0.0001080537],"category_scores_gemma":[0.00001621915,0.0000706225,0.00003260751,0.0004293036,0.00001367638,0.00006406593,0.00001496917,0.00004337074,0.00001078755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002509734,"about_ca_system_score_gemma":0.000004356793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000152623,"about_ca_topic_score_gemma":0.00001025942,"domain_scores_codex":[0.9995325,0.00002384691,0.0001407252,0.0001412746,0.00006677247,0.00009491245],"domain_scores_gemma":[0.9997259,0.0000314123,0.00002081208,0.0001390565,0.00004806777,0.00003473442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[7.022919e-7,0.00001094527,0.0001295615,0.00005145111,0.0001083022,0.000001114419,0.0001322969,0.0002132306,0.9241874,0.0005514716,0.000005000257,0.0746085],"study_design_scores_gemma":[0.00006968006,0.000002799698,0.3116452,0.000005709285,0.0001231834,0.000003538416,0.0001089443,0.5520836,0.1350327,0.0008021212,0.00001731705,0.0001051478],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8704877,0.000004943964,0.1287398,0.00008097538,0.00003787156,0.00006388946,0.000003040275,0.0001696814,0.0004120967],"genre_scores_gemma":[0.9398754,0.000006229171,0.05996342,0.00000680557,0.00005171744,0.000008669278,0.00002865231,0.000009410862,0.00004971843],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7891547,"threshold_uncertainty_score":0.2879902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01059052813941784,"score_gpt":0.2299262990328187,"score_spread":0.2193357708934008,"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."}}