{"id":"W2318126867","doi":"10.1139/x11-061","title":"A landscape-level analysis of yellow-cedar decline in coastal British Columbia","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Logistic regression; Geography; Physical geography; Elevation (ballistics); Snowpack; Predictability; Climate change; Digital elevation model; Ecology; Forestry; Climatology; Snow; Biology; Statistics; Geology; Meteorology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.0002143034,0.0001654954,0.000232507,0.001114073,0.0008450586,0.0007639356,0.0003643805,0.0001970557,0.001438949],"category_scores_gemma":[0.000794412,0.0001059492,0.0002117577,0.002400285,0.0003020021,0.000143764,0.0003677909,0.0002973483,0.000162257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004633517,"about_ca_system_score_gemma":0.002142762,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9125881,"about_ca_topic_score_gemma":0.969272,"domain_scores_codex":[0.9998537,0.00002055196,0.000007343109,0.0000350641,0.0000357611,0.00004744811],"domain_scores_gemma":[0.9994233,0.00008018773,0.00006754894,0.00003812401,0.0002863735,0.0001045362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007553591,0.00003594183,0.9859957,0.00001552921,0.00006500174,0.0002197857,0.0002266279,0.001831225,0.001181663,0.0000899317,0.0006900891,0.009572988],"study_design_scores_gemma":[0.000001504189,0.000004612666,0.9980464,0.000003341989,0.000007503099,0.00002154969,0.0002755589,0.001362001,0.00003065291,0.00001277123,0.0002312966,0.000002821067],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984717,0.00005122568,0.00006579296,0.00002904089,7.953565e-7,0.000006018217,0.0006399478,0.000007041956,0.00072845],"genre_scores_gemma":[0.9981169,0.0000469891,0.000168395,0.00001339057,7.026003e-7,0.000006887149,0.00114901,0.000003037911,0.0004947463],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08741194,"threshold_uncertainty_score":0.1758534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04651240218525307,"score_gpt":0.2729107595306182,"score_spread":0.2263983573453651,"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."}}