{"id":"W4200437277","doi":"10.5194/gi-2021-33","title":"Leveling airborne geophysical data using a unidirectional variational model","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Department of Science and Technology of Jilin Province; People's Government of Jilin Province","keywords":"Robustness (evolution); Computer science; Data processing; Data pre-processing; Property (philosophy); Remote sensing; Preprocessor; Algorithm; Data mining; Geophysics; Geology; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006991242,0.0003938366,0.0004226308,0.0004389964,0.0002524143,0.0008011502,0.0008698829,0.0005081105,0.001023778],"category_scores_gemma":[0.001652054,0.0003866302,0.0006726124,0.0006499506,0.0005170075,0.001031085,0.0007561824,0.0007465709,0.0002126822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005891336,"about_ca_system_score_gemma":0.0007676819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01032398,"about_ca_topic_score_gemma":0.008253488,"domain_scores_codex":[0.9996699,0.00008009379,0.00001977064,0.00009860605,0.00009913436,0.00003243707],"domain_scores_gemma":[0.9996532,0.0001465584,0.00004916039,0.00005332271,0.00007722009,0.0000204983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005013593,0.00004283685,0.002740017,0.00005755929,0.00004449951,0.00005776017,0.0001133446,0.8972782,0.01374213,0.02759975,0.0006885806,0.05758516],"study_design_scores_gemma":[0.000001245127,0.000004556055,0.00009146315,7.006619e-7,0.000001305328,0.000003141216,0.000002674905,0.9983702,0.0004229109,0.0009289131,0.0001705493,0.000002285278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01956553,0.00002477035,0.9797369,0.00006319643,0.00001107534,0.00001656644,0.00005605636,0.00009988335,0.0004260794],"genre_scores_gemma":[0.5431525,0.0001628911,0.4512042,0.00009548289,0.00003523328,0.000112399,0.0006221363,0.0001688904,0.00444624],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01032398,"threshold_uncertainty_score":0.02052778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06829070313876916,"score_gpt":0.2851422673093817,"score_spread":0.2168515641706125,"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."}}