{"id":"W2153346883","doi":"10.1093/jof/104.6.316","title":"Challenges in Visualizing Forests and Landscapes","year":2006,"lang":"en","type":"article","venue":"Journal of Forestry","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"U.S. Forest Service; Clemson University; University of Alberta; Joint Fire Science Program; University of Toledo; U.S. Department of Agriculture","keywords":"Visualization; Computer science; Data science; Process (computing); Natural resource management; Resource (disambiguation); Quality (philosophy); Natural resource; Data mining; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01388096,0.0008211523,0.001847015,0.003833365,0.003463689,0.02624702,0.003380138,0.005224572,0.005765908],"category_scores_gemma":[0.0585068,0.001695032,0.001274337,0.005759195,0.005797333,0.02898021,0.006922206,0.006336495,0.00164175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002083382,"about_ca_system_score_gemma":0.002890052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0125642,"about_ca_topic_score_gemma":0.0221095,"domain_scores_codex":[0.9919754,0.004384619,0.0005562203,0.0007496499,0.001994616,0.0003395246],"domain_scores_gemma":[0.9664228,0.02371683,0.0008729082,0.003021915,0.00451565,0.001449895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002181856,0.0001374107,0.008226581,0.001711592,0.0001791609,0.00049272,0.01030712,0.0314405,0.005551894,0.3812457,0.06880848,0.4916807],"study_design_scores_gemma":[0.00003800629,0.00003733301,0.00227316,0.0004802604,0.00006777875,0.001064651,0.01118414,0.0991405,0.003270332,0.7102773,0.1720493,0.0001172419],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05779975,0.04242647,0.6899736,0.1778059,0.001744416,0.0001846276,0.001452696,0.003446305,0.02516619],"genre_scores_gemma":[0.3402793,0.02830353,0.6189776,0.00310545,0.001385894,0.000241102,0.001082619,0.001504919,0.005119623],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02624702,"threshold_uncertainty_score":0.07341039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03645958427308339,"score_gpt":0.310845396471,"score_spread":0.2743858121979166,"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."}}