{"id":"W4402474617","doi":"10.5194/isprs-archives-xlviii-m-4-2024-41-2024","title":"Hyperspectral Remote Sensing of Potato Plant Nutrient Deprivation and Vegetation Stress using High-Resolution Spectroradiometry for Minimal Input Agricultural Systems","year":2024,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Water Quality Monitoring and Analysis","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Lethbridge","funders":"University of Lethbridge","keywords":"Hyperspectral imaging; Vegetation (pathology); Remote sensing; Environmental science; Agriculture; Nutrient; High resolution; Agronomy; Agricultural engineering; Geography; Biology; Ecology; Engineering; Archaeology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003537443,0.0002589068,0.0002899877,0.0002271698,0.000193043,0.0003364503,0.0002298801,0.0002701349,0.0003473013],"category_scores_gemma":[0.0001933879,0.0001571552,0.0002626527,0.0002348275,0.0001963559,0.0003126999,0.0001947665,0.0003687446,0.0001140568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002518321,"about_ca_system_score_gemma":0.0001510195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001801093,"about_ca_topic_score_gemma":0.003751452,"domain_scores_codex":[0.9998385,0.00003606087,0.000008063013,0.00005622921,0.00004842156,0.00001277586],"domain_scores_gemma":[0.9998455,0.00003949627,0.00003512667,0.00001978509,0.00004695432,0.00001308189],"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":[0.0001937895,0.00009354264,0.006618747,0.00009343198,0.0000236259,0.00002579313,0.00007341294,0.002210558,0.9816358,0.00004469022,0.00009795268,0.008888728],"study_design_scores_gemma":[0.00005831793,0.001227005,0.241845,0.00002755401,0.0001428745,0.0001995699,0.000517789,0.09614491,0.6579475,0.0002712512,0.001538394,0.0000797012],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870613,0.0001372998,0.01174911,0.00003987775,0.00000630933,0.00002580276,0.0001637567,0.0001343316,0.000682122],"genre_scores_gemma":[0.9824317,0.00009516188,0.01688404,0.00005205557,0.000002998418,0.00004070654,0.0002014761,0.00002208179,0.0002697116],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001801093,"threshold_uncertainty_score":0.003581166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01944531184815841,"score_gpt":0.2517051918248903,"score_spread":0.2322598799767319,"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."}}