{"id":"W2772052982","doi":"10.1109/jstars.2017.2773625","title":"Retrieving Leaf and Canopy Water Content of Winter Wheat Using Vegetation Water Indices","year":2017,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"National Key Research and Development Program of China; Higher Education Discipline Innovation Project","keywords":"Canopy; Environmental science; Remote sensing; Vegetation (pathology); Water content; Mathematics; Leaf area index; Winter wheat; Reflectivity; Agronomy; Botany; Physics; Biology; Geography; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003736325,0.0001328972,0.0002610004,0.00008325977,0.0002954064,0.0001173089,0.00009444266,0.0001169924,0.000004656233],"category_scores_gemma":[0.00005677064,0.00008202132,0.00003028254,0.00008952222,0.0001819666,0.0002569331,0.00006275331,0.000291547,6.381453e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007079062,"about_ca_system_score_gemma":0.00001524438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003442481,"about_ca_topic_score_gemma":0.0005840747,"domain_scores_codex":[0.9988318,0.00003940049,0.0004706662,0.0001648,0.0002758986,0.0002174452],"domain_scores_gemma":[0.9993173,0.00002707299,0.0003133081,0.0001554367,0.0001256834,0.00006112256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000024345,0.000008799122,0.008373908,0.00002588284,0.00002036624,0.00001096118,0.001520788,0.001196567,0.9670071,0.000004244176,0.000007495519,0.02179952],"study_design_scores_gemma":[0.0008081113,0.0000646851,0.4758112,0.0004017241,0.00005644331,0.0002138487,0.000208198,0.02212374,0.4991195,0.0005412381,0.0004366631,0.000214691],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972199,0.00003712448,0.001520314,0.0005080043,0.0002142721,0.0001244314,4.347324e-7,0.000004918043,0.0003706088],"genre_scores_gemma":[0.9626401,0.00007444701,0.03700193,0.00006648197,0.0001219672,7.934127e-9,0.000001431253,0.000009880639,0.00008374866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4678877,"threshold_uncertainty_score":0.3344732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0365745994987912,"score_gpt":0.2343850586216408,"score_spread":0.1978104591228496,"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."}}