{"id":"W2025781128","doi":"10.1109/jstars.2013.2251610","title":"Towards the Operational Use of Satellite Hyperspectral Image Data for Mapping Nutrient Status and Fertilizer Requirements in Australian Plantation Forests","year":2013,"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":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Forest and Wood Products Australia","keywords":"Fertilizer; Pinus radiata; Hyperspectral imaging; Nutrient; Canopy; Remote sensing; Environmental science; Computer science; Forestry; Agronomy; Geography; Ecology; Biology","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":[0.004369716,0.0004236127,0.0002391158,0.001413331,0.0003714141,0.001486723,0.0008656716,0.0004501797,0.0007376376],"category_scores_gemma":[0.004369151,0.0003788372,0.000323303,0.001401137,0.0004130672,0.001477242,0.001012776,0.0004871176,0.0002844878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001479465,"about_ca_system_score_gemma":0.001737881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1547938,"about_ca_topic_score_gemma":0.1546712,"domain_scores_codex":[0.9991444,0.0003154935,0.00006572888,0.0001488654,0.000278079,0.00004729166],"domain_scores_gemma":[0.9972555,0.0004905466,0.00030128,0.0002789557,0.001539188,0.0001346369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002944462,0.0003523477,0.2966454,0.0005986951,0.000135416,0.00027179,0.002297923,0.02665433,0.05061511,0.003622203,0.005953993,0.6125584],"study_design_scores_gemma":[0.0000547805,0.0004066341,0.6737797,0.0004327653,0.0001523746,0.0002540665,0.002408103,0.2575205,0.0275649,0.00426067,0.03302596,0.0001395986],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.813185,0.002061643,0.1553996,0.002547269,0.00004343662,0.0008569161,0.003486971,0.001602493,0.02081659],"genre_scores_gemma":[0.6234332,0.00116548,0.3695542,0.0003136587,0.00001789555,0.0002415619,0.002005434,0.00009550436,0.003173042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1547938,"threshold_uncertainty_score":0.3077856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07508109384605574,"score_gpt":0.266603845660585,"score_spread":0.1915227518145292,"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."}}