{"id":"W2136660332","doi":"10.1016/s0034-4257(02)00182-7","title":"Spatial analysis of radiometric fractions from high-resolution multispectral imagery for modelling individual tree crown and forest canopy structure and health","year":2003,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":115,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University; Defence Research and Development Canada","funders":"Canadian Forest Service; Natural Sciences and Engineering Research Council of Canada; Ministry of Natural Resources; University of Pittsburgh","keywords":"Multispectral image; Remote sensing; Canopy; Tree canopy; Variogram; Environmental science; Microsite; Forest inventory; Image resolution; Mathematics; Semivariance; Scale (ratio); Forest management; Spatial variability; Statistics; Geography; Kriging; Computer science; Cartography; Artificial intelligence; Agroforestry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002648736,0.0002245105,0.0004911022,0.0002583512,0.0001790886,0.00002347359,0.00006383189,0.0001338999,0.00002943056],"category_scores_gemma":[0.0000445396,0.0002029853,0.0001093905,0.0004889874,0.0002754883,0.00009847671,0.00005741811,0.000154679,6.218653e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003021673,"about_ca_system_score_gemma":0.00001464272,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01680875,"about_ca_topic_score_gemma":0.002021936,"domain_scores_codex":[0.9982333,0.0001024635,0.0004481752,0.0004951025,0.0004132602,0.0003077519],"domain_scores_gemma":[0.9990113,0.0001520001,0.0004072079,0.0002824043,0.000008020646,0.0001390591],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00004500473,0.00007549262,0.0186447,0.00002767672,0.0005706139,0.000003641807,0.0008411397,0.8096611,0.05631019,0.00001821812,0.00008064767,0.1137216],"study_design_scores_gemma":[0.0004937487,0.0000864894,0.5227419,0.00002144056,0.0004823108,0.000009213877,0.00005059172,0.4694852,0.005877929,0.0004484071,0.0001209177,0.0001818801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7379066,0.0002142654,0.2613138,0.0001047095,0.0000630209,0.0002634337,0.00009788485,0.000009041382,0.00002720264],"genre_scores_gemma":[0.7589203,0.0001763923,0.2407347,0.00002600473,0.00002570999,4.41263e-8,0.00008807635,0.00001456207,0.0000142468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5040972,"threshold_uncertainty_score":0.9897384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01729303632099407,"score_gpt":0.2286936816210922,"score_spread":0.2114006453000981,"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."}}