{"id":"W4379933000","doi":"10.1016/j.ecoinf.2023.102149","title":"Comparing phenocam color indices with phenological observations of black spruce in the boreal forest","year":2023,"lang":"en","type":"article","venue":"Ecological Informatics","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Youth Innovation Promotion Association; Fonds de recherche du Québec – Nature et technologies; Youth Innovation Promotion Association of the Chinese Academy of Sciences; Chinese Academy of Sciences; National Natural Science Foundation of China; Ministère des Forêts, de la Faune et des Parcs; China Scholarship Council; Université du Québec à Chicoutimi","keywords":"Phenology; Evergreen; Black spruce; Canopy; Growing season; Taiga; Environmental science; Annual growth cycle of grapevines; Tree canopy; Biology; Ecology; Horticulture","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":[0.0004308039,0.0001257402,0.0002096619,0.00003077535,0.0001068455,0.0000399305,0.0004390233,0.0001229324,0.0000565842],"category_scores_gemma":[0.0001595667,0.0000594229,0.00003416747,0.000926817,0.0004875251,0.0002109735,0.0002196897,0.0002722763,0.0002611321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007256652,"about_ca_system_score_gemma":0.00001032012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006676819,"about_ca_topic_score_gemma":0.001817831,"domain_scores_codex":[0.9987749,0.00006122713,0.0004228914,0.00009731936,0.0003441449,0.0002994607],"domain_scores_gemma":[0.9991933,0.0003698721,0.0001980941,0.0001831674,0.00001263143,0.00004290503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002031971,0.0001669839,0.8546168,0.00002159897,0.00001050693,0.00001501413,0.005981256,0.1310903,0.00007720599,0.00193286,0.005703278,0.0003639558],"study_design_scores_gemma":[0.0002209754,0.0002061424,0.9325083,0.0000110896,0.00000743566,0.000007280193,0.003636509,0.0617046,0.00001414612,0.0007506562,0.0008391004,0.00009371823],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.962687,0.000001029026,0.00006291664,0.0005224174,0.00002901417,0.0003468891,0.000002524089,0.000064876,0.03628332],"genre_scores_gemma":[0.9931175,0.000009032436,0.006317161,0.0004447362,0.00001560581,0.000006416386,0.00003339797,0.000003353367,0.00005283729],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07789161,"threshold_uncertainty_score":0.3356412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04648786537007668,"score_gpt":0.2384565688083678,"score_spread":0.1919687034382911,"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."}}