{"id":"W2218306977","doi":"","title":"Quantifying Equity with Messrs. Markov, Lorenz and Gini: A Case Study of Dunster, British Columbia","year":2014,"lang":"en","type":"article","venue":"","topic":"Conservation, Biodiversity, and Resource Management","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Equity (law); Natural resource; Revenue; Logging; Economics; Markov chain; Business; Finance; Geography; Political science; Forestry; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.003465825,0.0003477861,0.0005728375,0.00161961,0.004286905,0.004481219,0.001682239,0.001188857,0.004195266],"category_scores_gemma":[0.01313254,0.0003169487,0.0002746471,0.004825236,0.003374942,0.001499302,0.001858709,0.001882839,0.000161471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04427311,"about_ca_system_score_gemma":0.01436094,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9594766,"about_ca_topic_score_gemma":0.9758897,"domain_scores_codex":[0.9982237,0.0008474419,0.00004668382,0.0001490003,0.0002813199,0.0004519039],"domain_scores_gemma":[0.9935035,0.004440536,0.0003733429,0.000261963,0.0008800731,0.0005406375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004828663,0.0006498073,0.6142068,0.000361574,0.0003105314,0.007887653,0.03741878,0.05588697,0.0006007524,0.129971,0.02579039,0.1264328],"study_design_scores_gemma":[0.0001071632,0.0002897481,0.5987977,0.0008285387,0.0004093038,0.001246018,0.2128803,0.08905564,0.001168188,0.04529306,0.04961774,0.0003067412],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9688336,0.001428508,0.001082486,0.003045841,0.00001382406,0.00006257896,0.0003222185,0.00001288494,0.025198],"genre_scores_gemma":[0.9957997,0.00042388,0.0004669836,0.00006233563,0.000002857488,0.00001386174,0.0000629709,0.00000886167,0.00315854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04427311,"threshold_uncertainty_score":0.3212254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0288590393767074,"score_gpt":0.2351883378798434,"score_spread":0.206329298503136,"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."}}