{"id":"W2040775997","doi":"10.1111/avsc.12122","title":"Mapping continuous forest type variation by means of correlating remotely sensed metrics to canopy N:P ratio in a boreal mixedwood forest","year":2014,"lang":"en","type":"article","venue":"Applied Vegetation Science","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Forest Service; Queen's University; Ontario Forest Research Institute; Ministry of Natural Resources and Forestry","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Canopy; Environmental science; Taiga; Remote sensing; Spatial variability; Tree canopy; Atmospheric sciences; Boreal; Ecology; Mathematics; Forestry; Geography; Geology; Statistics; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0004797495,0.0003002091,0.0001934875,0.0007410789,0.0002893559,0.0005061959,0.0003605588,0.0001824612,0.0003001129],"category_scores_gemma":[0.0007619297,0.0001254612,0.0002380146,0.0005717262,0.0002271975,0.0003185339,0.0001692201,0.0001171146,0.00007741185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007650043,"about_ca_system_score_gemma":0.0003496194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1458597,"about_ca_topic_score_gemma":0.2526697,"domain_scores_codex":[0.9998072,0.0000339923,0.00001255028,0.00007866495,0.0000385721,0.00002909898],"domain_scores_gemma":[0.9995058,0.0001330295,0.0001438687,0.00003641534,0.0001235708,0.00005742558],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001241317,0.00004914144,0.9788952,0.00001870461,0.00006708344,0.00005859693,0.0001318993,0.002614329,0.00921491,0.0000250798,0.0001016299,0.008699405],"study_design_scores_gemma":[0.000002641421,0.00002127443,0.9953274,0.000002279665,0.000008142281,0.00002968953,0.0001008205,0.004161885,0.0002612812,0.00001421489,0.00006684987,0.000003545712],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991056,0.00003929432,0.0004387145,0.000005033349,9.224234e-7,0.000004943749,0.0002202458,0.00001607734,0.0001690208],"genre_scores_gemma":[0.9988147,0.0000105617,0.0008930282,0.000003569756,0.000001050409,0.000004245644,0.0002367577,0.000002334177,0.00003373251],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1458597,"threshold_uncertainty_score":0.2900214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01036015848556387,"score_gpt":0.2309519878128118,"score_spread":0.2205918293272479,"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."}}