{"id":"W2791549826","doi":"10.1071/aseg2018abw9_3h","title":"VTEM ET: An improved helicopter time-domain EM system for near surface applications","year":2018,"lang":"en","type":"article","venue":"ASEG Extended Abstracts","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Petro Geotech (Canada)","funders":"","keywords":"Transmitter; Time domain; Broadband; Acoustics; Waveform; Bandwidth (computing); Distortion (music); Data acquisition; Electrical engineering; Electronic engineering; Computer science; Engineering; Physics; Telecommunications; Radar; Channel (broadcasting)","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":[0.0002828179,0.0002042619,0.0002125525,0.00001952008,0.0002013567,0.0001189749,0.0002266322,0.0001092507,0.00003401822],"category_scores_gemma":[0.00001192618,0.0002016247,0.00008582087,0.0001581636,0.00005598917,0.0001660285,0.00002238803,0.0001361044,0.0006241475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004603033,"about_ca_system_score_gemma":0.00002744842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001616889,"about_ca_topic_score_gemma":0.00001364037,"domain_scores_codex":[0.9988807,0.00002845736,0.0003069255,0.0003263037,0.0001070341,0.0003505734],"domain_scores_gemma":[0.9989152,0.0001333162,0.0000687363,0.0005666143,0.0001148766,0.0002012524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004735747,0.0005047921,0.000002800482,0.0004409327,0.0001579685,0.000002646105,0.0007279952,0.004658224,0.6843534,0.02094555,0.002176024,0.2859823],"study_design_scores_gemma":[0.003505181,0.0009852963,0.07657316,0.0002285653,0.0002771362,0.00005333138,0.001665833,0.3086584,0.3194909,0.0284183,0.2571585,0.002985341],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8257014,0.00007340523,0.1614704,0.0002619793,0.0002499511,0.002357951,0.000313734,0.001330124,0.008241085],"genre_scores_gemma":[0.861995,0.000001424116,0.1368964,0.00008062444,0.0003188583,0.0004278894,0.00009266518,0.00005965836,0.00012744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3648625,"threshold_uncertainty_score":0.8222018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0113209482116676,"score_gpt":0.2729885419246889,"score_spread":0.2616675937130213,"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."}}