{"id":"W2156459128","doi":"10.1093/mnras/stu2219","title":"Weighted principal component analysis: a weighted covariance eigendecomposition approach","year":2014,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Belgian Federal Science Policy Office; York University; Carnegie Mellon University; Office of Science; Johns Hopkins University; College of Engineering, Michigan State University; Harvard University; Ohio State University; National Science Foundation; University of Washington; Alfred P. Sloan Foundation; New Mexico State University; University of Portsmouth; Vanderbilt University; Yale University; University of Arizona; Princeton University; Brookhaven National Laboratory; U.S. Department of Energy","keywords":"Principal component analysis; Covariance; Extrapolation; Covariance matrix; Algorithm; Physics; Eigenvalues and eigenvectors; Applied mathematics; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.002029581,0.001632833,0.001104578,0.002256699,0.0006607012,0.001678622,0.001471926,0.0008028544,0.003267838],"category_scores_gemma":[0.005207285,0.0006286064,0.001232819,0.002675039,0.0008220299,0.001726942,0.001699031,0.001702294,0.002208951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003859664,"about_ca_system_score_gemma":0.001563387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002329984,"about_ca_topic_score_gemma":0.002486482,"domain_scores_codex":[0.9979368,0.0007223206,0.0001363953,0.0004493806,0.0006581284,0.0000968958],"domain_scores_gemma":[0.9982998,0.0005471483,0.0001471361,0.0002727247,0.0006777298,0.00005553651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009999193,0.0001475177,0.001792105,0.0003726609,0.0004256923,0.0001549107,0.0002292609,0.1194112,0.0226332,0.05059884,0.009871325,0.7942632],"study_design_scores_gemma":[0.00003089988,0.00008162056,0.001706836,0.00004712133,0.00008365469,0.0002057341,0.00006296847,0.9116451,0.008053732,0.05941729,0.01855885,0.0001062241],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008920085,0.0001054561,0.9983509,0.00003880179,0.00002114455,0.0000238335,0.00004858664,0.0002076753,0.0003117102],"genre_scores_gemma":[0.02086115,0.0003208264,0.9769223,0.00004956559,0.00007242248,0.0001673828,0.0003387763,0.0001958638,0.001071686],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003267838,"threshold_uncertainty_score":0.01093197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005772562376542998,"score_gpt":0.1865396236073439,"score_spread":0.1807670612308009,"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."}}