{"id":"W1505923981","doi":"","title":"Least Squares Spectral Analysis and Its Application to Superconducting Gravimeter Data Analysis","year":2004,"lang":"en","type":"article","venue":"地球空间信息科学学报：英文版","topic":"Scientific Research and Discoveries","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Gravimeter; Geodetic datum; Fast Fourier transform; Noise (video); Geodesy; Spectral analysis; Sampling (signal processing); Fourier analysis; Time series; Remote sensing; Fourier transform; Mathematics; Computer science; Algorithm; Physics; Statistics; Geography; Geophysics; Mathematical analysis; Telecommunications; Artificial intelligence; Spectroscopy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003509427,0.0001624609,0.0003018104,0.0005000596,0.0002371759,0.0003381255,0.0004734055,0.00002704459,0.0004809591],"category_scores_gemma":[0.00002828244,0.0001437679,0.0001612896,0.002644996,0.00006107731,0.0006108235,0.0002830546,0.0001130474,0.0001213745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002573453,"about_ca_system_score_gemma":0.00005515872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00528887,"about_ca_topic_score_gemma":0.001929797,"domain_scores_codex":[0.9982448,0.00003811013,0.0002416345,0.0007330799,0.0003442983,0.0003980965],"domain_scores_gemma":[0.9987043,0.00003670094,0.00005427417,0.0008776565,0.00007737757,0.0002496996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000103003,0.0005277118,0.84329,0.000042206,0.008809593,0.000009052511,0.006602732,0.01154436,0.06422497,0.03687909,0.0009258469,0.0270415],"study_design_scores_gemma":[0.003358596,0.0003582846,0.7049782,0.00005178579,0.01353197,0.000004962772,0.04434894,0.0298483,0.1728397,0.01815099,0.0095549,0.002973331],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9399932,0.00007897191,0.05748043,0.0003953569,0.00003834108,0.0002126601,0.0003869975,0.00002576981,0.001388241],"genre_scores_gemma":[0.9970164,0.000002291189,0.001288746,0.00003295059,0.000183815,0.00002813895,0.0009673077,0.00001012489,0.0004702547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1383117,"threshold_uncertainty_score":0.7995225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03838721822786705,"score_gpt":0.3294113202628494,"score_spread":0.2910241020349823,"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."}}