{"id":"W2793208425","doi":"10.1071/aseg2018abm3_1e","title":"Validating the Gedex HD-AGG™ Airborne Gravity Gradiometer","year":2018,"lang":"en","type":"article","venue":"ASEG Extended Abstracts","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Gedex (Canada)","funders":"","keywords":"Gradiometer; Noise (video); Terrain; Remote sensing; SIGNAL (programming language); Acoustics; Environmental science; Computer science; Geodesy; Physics; Geology; Geography; Artificial intelligence; Magnetic field; Cartography","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":["insufficient_payload"],"category_scores_codex":[0.000814768,0.0001965182,0.0002153254,0.00006076246,0.0004387835,0.0001235048,0.0003970577,0.00008382773,0.001397589],"category_scores_gemma":[0.0004205219,0.0001158408,0.0001191352,0.0005809714,0.0002391454,0.000199963,0.00002145121,0.0003466412,0.001945258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004456754,"about_ca_system_score_gemma":0.00003576982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001803666,"about_ca_topic_score_gemma":0.0005197817,"domain_scores_codex":[0.998131,0.0002031616,0.0003022678,0.0003759387,0.0003994355,0.0005882405],"domain_scores_gemma":[0.9984833,0.0007101338,0.0001325884,0.0003654491,0.00007967642,0.0002288242],"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":[0.00002401492,0.0000413843,0.000496521,0.000005479596,0.00001904429,0.00001668836,0.00006455443,0.00004785679,0.0001243895,0.000203572,0.0008921213,0.9980644],"study_design_scores_gemma":[0.0001409965,0.0002604435,0.9505458,0.0000075799,0.0000216889,0.00001946224,0.00002589144,0.0008118577,0.004810064,0.03537701,0.007791816,0.0001874236],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829901,0.0001277022,0.0002800114,0.00130645,0.0006206775,0.0001938107,0.00003233086,0.000100574,0.01434836],"genre_scores_gemma":[0.9942032,0.000006856494,0.003377587,0.00101267,0.0008550214,0.000001924718,0.00003297135,0.000005311024,0.00050446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9978769,"threshold_uncertainty_score":0.9995153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02340696466826561,"score_gpt":0.2649958748692676,"score_spread":0.241588910201002,"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."}}