{"id":"W2920846280","doi":"10.3390/rs11060636","title":"Geographically Weighted Regression Effects on Soil Zinc Content Hyperspectral Modeling by Applying the Fractional-Order Differential","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Fujian Normal University; Fujian Provincial Department of Science and Technology; Erasmus+; Ministry of Science and Technology of the People's Republic of China","keywords":"Hyperspectral imaging; Statistics; Soil science; Mathematics; Multicollinearity; Logarithm; Ordinary least squares; Content (measure theory); Regression analysis; Environmental science; Remote sensing; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001332247,0.0006025991,0.0004690654,0.0008150025,0.0003155975,0.0004886392,0.001131901,0.0004049586,0.0007266332],"category_scores_gemma":[0.002425018,0.0002467466,0.001232053,0.0009229647,0.0004802116,0.0005627255,0.0007467435,0.0004304894,0.0001088174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005781003,"about_ca_system_score_gemma":0.0005337949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01423679,"about_ca_topic_score_gemma":0.008406751,"domain_scores_codex":[0.9993268,0.0002468938,0.00003366829,0.0001970934,0.0001357673,0.00005963815],"domain_scores_gemma":[0.9994342,0.0003170257,0.00009892665,0.00005340908,0.00008210816,0.00001444884],"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":[0.0001378862,0.0001227095,0.0211337,0.0001083191,0.0002343509,0.0003645324,0.0002275637,0.8737735,0.01569884,0.01024125,0.0003136216,0.07764376],"study_design_scores_gemma":[0.000004248587,0.00001509125,0.002335506,0.000002569495,0.00002194899,0.00002138294,0.0000161757,0.995003,0.001440718,0.000765074,0.0003645993,0.00000969559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3017986,0.0002438867,0.6960801,0.0001102336,0.00002486617,0.00006870853,0.0001252642,0.0003568584,0.001191419],"genre_scores_gemma":[0.8970402,0.0003366594,0.1005363,0.00003592983,0.00001651998,0.00009669853,0.0001991338,0.0000643418,0.001674295],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01423679,"threshold_uncertainty_score":0.0283078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01118091693384718,"score_gpt":0.2185043182589483,"score_spread":0.2073234013251011,"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."}}