{"id":"W2010148132","doi":"10.1002/joc.1397","title":"Climate Change detection over different land surface vegetation classes","year":2006,"lang":"en","type":"article","venue":"International Journal of Climatology","topic":"Climate variability and models","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Aeronautics and Space Administration","keywords":"Climatology; Environmental science; Vegetation (pathology); Forcing (mathematics); Greenhouse gas; Land cover; Climate change; Climate model; Global warming; Deforestation (computer science); Physical geography; Atmospheric sciences; Land use; Geography; Geology","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":[0.0007587257,0.0002769014,0.000315002,0.001403374,0.0002248576,0.000653344,0.000221213,0.0002256456,0.0008486536],"category_scores_gemma":[0.001623673,0.0001293262,0.0004053741,0.001264658,0.0001733339,0.0003477887,0.0004154476,0.0002716683,0.0002285725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003989386,"about_ca_system_score_gemma":0.0002860613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01086292,"about_ca_topic_score_gemma":0.01109864,"domain_scores_codex":[0.9996048,0.00007283126,0.00002468872,0.0001184906,0.0001090396,0.00007029569],"domain_scores_gemma":[0.9988575,0.00037971,0.0002364404,0.0001209115,0.00030195,0.000103402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003083048,0.00006727183,0.9511663,0.0000489342,0.0001341528,0.00006868158,0.0001362935,0.01548446,0.005481035,0.0002863163,0.001104186,0.02571403],"study_design_scores_gemma":[0.00001917453,0.00006332312,0.9366605,0.000009823612,0.00004346961,0.0000748951,0.0001179319,0.05737775,0.004010156,0.0002833934,0.0013223,0.00001732584],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938632,0.00008115264,0.001261488,0.00003733076,0.000008527898,0.00001444204,0.00315347,0.0001371256,0.001443238],"genre_scores_gemma":[0.9949901,0.00002495794,0.0009553401,0.0000091448,0.000004342701,0.000008398089,0.003870986,0.00001061476,0.0001260524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01086292,"threshold_uncertainty_score":0.02159935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01814110986933544,"score_gpt":0.2722829723856719,"score_spread":0.2541418625163365,"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."}}