{"id":"W2555863151","doi":"10.1016/j.agrformet.2016.11.011","title":"Large-scale estimation of xylem phenology in black spruce through remote sensing","year":2016,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Tree-ring climate responses","field":"Earth and Planetary Sciences","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Phenology; Normalized Difference Vegetation Index; Growing season; Moderate-resolution imaging spectroradiometer; Xylem; Environmental science; Black spruce; Taiga; Growing degree-day; Physical geography; Boreal; Climatology; Remote sensing; Climate change; Atmospheric sciences; Geography; Ecology; Biology; Geology; Satellite; Botany","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":[0.0003398841,0.000245268,0.0001606392,0.0004308856,0.000156765,0.0002352332,0.0002026607,0.0001969122,0.0002625861],"category_scores_gemma":[0.0004235447,0.0001414122,0.000159622,0.0003283967,0.00009989021,0.0003326842,0.0001747685,0.000119295,0.0000962385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002533377,"about_ca_system_score_gemma":0.0002199928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02736137,"about_ca_topic_score_gemma":0.06402132,"domain_scores_codex":[0.9999274,0.00001644662,0.000002857819,0.0000293443,0.00001134587,0.00001261836],"domain_scores_gemma":[0.99976,0.00008268531,0.00004625398,0.00002398282,0.00005169411,0.0000354163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00039388,0.0003350288,0.8518377,0.00005059601,0.0001561925,0.0001118431,0.0002885721,0.01760332,0.08067882,0.0001185503,0.0004881842,0.04793732],"study_design_scores_gemma":[0.00001094772,0.00003702943,0.9620621,0.000003879975,0.00002555149,0.00003293086,0.0001159391,0.03566726,0.001769405,0.00006058432,0.0002044924,0.000009947326],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989556,0.00005740251,0.0006691177,0.00001143715,0.000001669929,0.000002240066,0.0001762139,0.00002041842,0.0001058214],"genre_scores_gemma":[0.9980811,0.00002795488,0.001321091,0.000005187354,0.000002784859,0.000002700329,0.0004203186,0.000003497139,0.0001353903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02736137,"threshold_uncertainty_score":0.0544042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01303548758083317,"score_gpt":0.2222895092958207,"score_spread":0.2092540217149876,"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."}}