{"id":"W3011608264","doi":"10.3390/rs12060922","title":"Phenology-Based Mapping of an Alien Invasive Species Using Time Series of Multispectral Satellite Data: A Case-Study with Glossy Buckthorn in Québec, Canada","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Ministère des Ressources naturelles et des Forêts; Université de Montréal; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère des Forêts, de la Faune et des Parcs","keywords":"Phenology; Normalized Difference Vegetation Index; Invasive species; Evergreen; Remote sensing; Geography; Forestry; Vegetation (pathology); Biodiversity; Ecology; Environmental science; Physical geography; Biology; Climate change","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":[0.0002586219,0.0005161348,0.0002769553,0.001467721,0.001364154,0.0009741641,0.0009629137,0.000401495,0.0009029494],"category_scores_gemma":[0.000531958,0.0001718986,0.0003852935,0.003080178,0.0004906703,0.0002563513,0.0003827574,0.0003610376,0.0001255547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01620069,"about_ca_system_score_gemma":0.00902959,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9955732,"about_ca_topic_score_gemma":0.9979956,"domain_scores_codex":[0.9997533,0.00001538229,0.000008852089,0.00006255435,0.00007280472,0.00008714714],"domain_scores_gemma":[0.9993906,0.00007218179,0.00007320522,0.00002383444,0.0003382791,0.0001020362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001908252,0.0002706405,0.9286075,0.0002020727,0.000183013,0.003379971,0.0031011,0.0108035,0.01297993,0.0003635124,0.002328824,0.03758902],"study_design_scores_gemma":[0.00001297195,0.0000380054,0.9743052,0.00003418335,0.0000431235,0.0002294554,0.003372964,0.01931197,0.000625348,0.00003551467,0.001954727,0.00003647339],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949409,0.0002925208,0.0007711631,0.0001073405,0.000004670606,0.00007169734,0.001992942,0.00003334529,0.001785467],"genre_scores_gemma":[0.9951191,0.0002176252,0.00194778,0.00003742231,0.000002763584,0.00001910366,0.001429687,0.000008892562,0.001217674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01620069,"threshold_uncertainty_score":0.1175448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03180688217623468,"score_gpt":0.2216114585861136,"score_spread":0.1898045764098789,"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."}}