{"id":"W2333399440","doi":"10.14358/pers.78.2.161","title":"Integrating Remote Sensing and Wavelet Analysis for Studying Fine-scaled Vegetation Spatial Variation among Three Different Ecosystems","year":2012,"lang":"en","type":"article","venue":"Photogrammetric Engineering & Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"University of Toronto","keywords":"Remote sensing; Spatial variability; Wavelet; Vegetation (pathology); Geography; Variation (astronomy); Cartography; Ecosystem; Environmental science; Physical geography; Ecology; Computer science; Statistics; Mathematics; Artificial intelligence; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001063029,0.000573532,0.0007242363,0.0006690961,0.000359718,0.0002503688,0.0001359134,0.0002609506,0.000006099463],"category_scores_gemma":[0.0008494836,0.0005045072,0.0002993719,0.002678339,0.00005574265,0.0003209682,0.00016076,0.000444566,0.000006861066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006019457,"about_ca_system_score_gemma":0.000006608173,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01433724,"about_ca_topic_score_gemma":0.01105076,"domain_scores_codex":[0.9967934,0.00011973,0.0007596426,0.00071812,0.0006388281,0.0009702386],"domain_scores_gemma":[0.9980668,0.0006666456,0.0004217101,0.0004425494,0.00008143311,0.0003208535],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002233883,0.00002796095,0.009397238,0.000139468,0.000411924,0.000008674248,0.001853558,0.03834255,0.07939855,0.000002939781,0.00001232827,0.8703825],"study_design_scores_gemma":[0.0004264168,0.00004279177,0.1674255,0.0001430448,0.0005369298,0.00004018876,0.0000831936,0.8268996,0.003802831,0.00003534284,0.00006879569,0.0004953019],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4722169,0.00007130054,0.5264843,0.00001640177,0.0004263574,0.0005423887,0.000001717081,0.0001564289,0.00008422922],"genre_scores_gemma":[0.7726874,0.000007727958,0.2268074,0.00001752789,0.0003578626,1.401971e-7,0.00004337362,0.0000650454,0.0000135118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8698872,"threshold_uncertainty_score":0.9997407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01156062384973802,"score_gpt":0.2071772374160656,"score_spread":0.1956166135663276,"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."}}