{"id":"W2130725901","doi":"10.1109/igarss.2002.1026774","title":"Spectral mixture analysis of potato crops under different irrigation regimes","year":2003,"lang":"en","type":"article","venue":"","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Remote sensing; Vegetation (pathology); Irrigation; Environmental science; Pixel; Normalized Difference Vegetation Index; Shadow (psychology); SMA*; Moisture stress; Water content; Reflectivity; Moisture; Soil science; Agronomy; Computer science; Geography; Geology; Artificial intelligence; Meteorology; Leaf area index; Geotechnical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0002260763,0.0001308515,0.0001976841,0.000507452,0.0001091908,0.0001798461,0.0001162518,0.0001042818,0.0003164363],"category_scores_gemma":[0.0004444221,0.00007827843,0.0001688743,0.0004137373,0.00008443114,0.0001436984,0.0001385045,0.0001075592,0.0000731749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001430511,"about_ca_system_score_gemma":0.00007127831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003554755,"about_ca_topic_score_gemma":0.00433741,"domain_scores_codex":[0.9999394,0.00001214654,0.000003695003,0.00001814309,0.00001624386,0.00001041289],"domain_scores_gemma":[0.9997908,0.00007100978,0.00003479614,0.00002032502,0.00006780055,0.00001522714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001917074,0.0001409795,0.1809407,0.0001353553,0.0002061579,0.0003241872,0.0005457041,0.02522535,0.6999363,0.000295463,0.0004926999,0.08983997],"study_design_scores_gemma":[0.00001396433,0.0002276894,0.8705232,0.000004508913,0.00007190289,0.0002102791,0.000298741,0.09965699,0.02811918,0.0003217447,0.0005236153,0.00002813514],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997892,0.00002798573,0.001830276,0.000006115942,7.782929e-7,0.000002363293,0.00008371208,0.00002500818,0.0001316584],"genre_scores_gemma":[0.9983671,0.00002586295,0.001148,0.000002248737,7.465075e-7,0.000004402401,0.0002943428,0.000006314574,0.0001509733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003554755,"threshold_uncertainty_score":0.007068157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006822997470642468,"score_gpt":0.2086262495054035,"score_spread":0.201803252034761,"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."}}