{"id":"W3098198702","doi":"10.1016/j.jenvman.2020.111617","title":"Extrapolating canopy phenology information using Sentinel-2 data and the Google Earth Engine platform to identify the optimal dates for remotely sensed image acquisition of semiarid mangroves","year":2020,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Coastal wetland ecosystem dynamics","field":"Environmental Science","cited_by":67,"is_retracted":false,"has_abstract":false,"ca_institutions":"Nipissing University","funders":"Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México","keywords":"Mangrove; Phenology; Canopy; Environmental science; Multispectral image; Normalized Difference Vegetation Index; Remote sensing; Deciduous; Vegetation (pathology); Geography; Earth observation; Ecology; Climate change; Satellite; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0003339514,0.0003233526,0.0002009394,0.0008796197,0.0002079313,0.0003991476,0.0002962275,0.0003366966,0.0006353653],"category_scores_gemma":[0.000854042,0.0001971145,0.0003989139,0.0006269888,0.00007262958,0.0005556491,0.0002589579,0.0003046505,0.0003009961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003111992,"about_ca_system_score_gemma":0.0006724884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02257175,"about_ca_topic_score_gemma":0.04285593,"domain_scores_codex":[0.9999168,0.000009224924,0.000006057307,0.0000287825,0.0000167785,0.00002232273],"domain_scores_gemma":[0.9997415,0.00006717959,0.00003289928,0.00002641956,0.0001012051,0.00003085645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004552839,0.0007825285,0.5293902,0.0002657517,0.000274381,0.0005169771,0.0003833755,0.1990809,0.0608605,0.0007293987,0.005609125,0.2016518],"study_design_scores_gemma":[0.00003684669,0.0001023138,0.3322054,0.00004473002,0.0001101847,0.00009347669,0.0003579034,0.6576808,0.006964041,0.0007334534,0.001627158,0.00004370364],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9861407,0.0002292349,0.01034293,0.00008551477,0.00003745681,0.00002270307,0.001359313,0.0003087989,0.00147334],"genre_scores_gemma":[0.9829682,0.0001069123,0.0149471,0.0000245888,0.00001103483,0.00001200724,0.001622846,0.00002914312,0.0002782688],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02257175,"threshold_uncertainty_score":0.04488075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01646572574996946,"score_gpt":0.243020385885385,"score_spread":0.2265546601354156,"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."}}