{"id":"W2029472371","doi":"10.1029/1999gl011243","title":"Capabilities of 3‐D wavelet transforms to detect plume‐like structures from seismic tomography","year":2000,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Plume; Geology; Wavelet; Noise (video); Gaussian; Seismology; Computational physics; Physics; Meteorology; Computer science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003256967,0.0001663067,0.0002561455,0.0002692249,0.0001771527,0.0000638365,0.0005159255,0.00006273416,0.003467864],"category_scores_gemma":[0.00004170873,0.0001293143,0.0001580841,0.0006068209,0.0005297675,0.0002006031,0.00001534261,0.0004341987,0.0003312886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001075684,"about_ca_system_score_gemma":0.00004376536,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07546063,"about_ca_topic_score_gemma":0.0001384558,"domain_scores_codex":[0.9976276,0.0001967375,0.0002336645,0.0004067746,0.0008818878,0.0006534],"domain_scores_gemma":[0.9988849,0.0004286167,0.00002100021,0.0003607387,0.00006152302,0.0002432816],"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":[0.0003400204,0.00002463519,0.001966371,0.00004368937,0.0000540943,0.00002026086,0.002147016,0.0004805146,0.01269646,0.00002002811,0.06407778,0.9181291],"study_design_scores_gemma":[0.001464044,0.002205112,0.4028608,0.0002408585,0.00004796868,0.00001035932,0.001694434,0.02179475,0.2002181,0.09119523,0.2767745,0.001493823],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936211,0.00007810489,0.0002625987,0.003923191,0.00008158272,0.0002730468,0.0002344874,0.00008906982,0.001436838],"genre_scores_gemma":[0.99203,0.00003370587,0.002253658,0.005249667,0.0001468115,0.000005405132,0.00006445721,0.000007341026,0.0002089297],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9166353,"threshold_uncertainty_score":0.9974431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01649307748162653,"score_gpt":0.253898235840237,"score_spread":0.2374051583586105,"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."}}