{"id":"W2592212735","doi":"","title":"Combining citizen science phenological observations with remote sensing data","year":2016,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Citizen science; Phenology; Computer science; Remote sensing; Geography; Ecology; Astronomy","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":[0.001944916,0.0007509073,0.0009019335,0.004358855,0.0003121221,0.002389956,0.0005342942,0.001317489,0.002904068],"category_scores_gemma":[0.005580524,0.0005481874,0.001098462,0.00632323,0.0003202705,0.002498353,0.001537546,0.0006920548,0.001757503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004881716,"about_ca_system_score_gemma":0.0007217735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01199573,"about_ca_topic_score_gemma":0.02634251,"domain_scores_codex":[0.9988285,0.0003507908,0.00005060937,0.0003553214,0.000295458,0.0001193774],"domain_scores_gemma":[0.9968831,0.001183539,0.0003074668,0.0008764621,0.0005997677,0.0001496441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004535655,0.0006091307,0.2659568,0.0008323838,0.00136691,0.0005940119,0.0007057423,0.08944634,0.06276691,0.001444252,0.01389844,0.5619256],"study_design_scores_gemma":[0.00009799986,0.0002568205,0.4807381,0.0001965974,0.0006423213,0.0003509918,0.002089341,0.4566013,0.01415355,0.01392439,0.03073718,0.0002114822],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7028042,0.00234423,0.2381788,0.003461057,0.0008060348,0.0003148188,0.02730155,0.003732746,0.02105651],"genre_scores_gemma":[0.8712367,0.001251191,0.1051283,0.0005729429,0.0004831118,0.0001303448,0.01724137,0.0002861757,0.003669738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01199573,"threshold_uncertainty_score":0.02385181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06856668925088072,"score_gpt":0.2597834148276094,"score_spread":0.1912167255767286,"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."}}