{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006225666,0.0002458561,0.0002318769,0.001056649,0.0001750891,0.0003588433,0.0002235105,0.0003055688,0.0002746937],"category_scores_gemma":[0.001051529,0.0001612659,0.0003519375,0.001283965,0.0001625103,0.0006064868,0.0002849664,0.000234424,0.00006665834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001697544,"about_ca_system_score_gemma":0.0002477433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002860274,"about_ca_topic_score_gemma":0.003871926,"domain_scores_codex":[0.9998742,0.00003879464,0.000007333553,0.00002926145,0.0000270108,0.00002326628],"domain_scores_gemma":[0.999577,0.0002502793,0.00004538834,0.00003762227,0.00005994324,0.00002973927],"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":[0.001364618,0.0007119213,0.2693526,0.0002206601,0.0004195054,0.0003517745,0.0006577706,0.04503094,0.3749361,0.001387614,0.0004037449,0.3051628],"study_design_scores_gemma":[0.00007216608,0.0003320069,0.6016715,0.00001420318,0.0003585062,0.0002689796,0.0005665596,0.3773358,0.01725293,0.00158264,0.000492355,0.00005234416],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9787836,0.000101312,0.02060727,0.00002237518,0.000008044939,0.00001132533,0.00005864962,0.00003255439,0.0003748619],"genre_scores_gemma":[0.9840941,0.00006576913,0.01561458,0.00001023836,0.000005399308,0.00001385229,0.00009071727,0.00001043138,0.00009492936],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002860274,"threshold_uncertainty_score":0.005687177,"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."}}