{"id":"W2413579047","doi":"","title":"Modelling British Columbia’s ecosystems and avian richness using landscape-scale indirect indicators of biodiversity","year":2012,"lang":"en","type":"dissertation","venue":"UVic’s Research and Learning Repository (University of Victoria)","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Space Agency; U.S. Forest Service; Canadian Forest Service; University of British Columbia; British Columbia Innovation Council","keywords":"Species richness; Biodiversity; Ecosystem; Geography; Scale (ratio); Environmental resource management; Ecology; Environmental science; Cartography; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003552123,0.0006032915,0.0002542383,0.0007930241,0.0007749183,0.001609198,0.001329875,0.0006074081,0.002303753],"category_scores_gemma":[0.001306933,0.0005233166,0.0005648155,0.00109591,0.0004896293,0.0005143711,0.0006292363,0.0005930012,0.0002074621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007030898,"about_ca_system_score_gemma":0.004248725,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8930085,"about_ca_topic_score_gemma":0.9096884,"domain_scores_codex":[0.9998294,0.00004616328,0.000007134708,0.00004834563,0.00002139446,0.00004742384],"domain_scores_gemma":[0.9995679,0.000178685,0.00005233947,0.00002834367,0.0001154727,0.00005729141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002896729,0.00003211457,0.04062865,0.00002963099,0.00006608835,0.00006961155,0.00008168562,0.9520537,0.0004544537,0.001201167,0.0007824664,0.004571433],"study_design_scores_gemma":[0.000009006121,0.000009019202,0.02155399,0.00001181735,0.0000244953,0.00001718224,0.0001231766,0.9768183,0.00009713336,0.0005758266,0.0007468217,0.00001325016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9734991,0.0002088544,0.01331287,0.0003693187,0.0000171418,0.00005997049,0.002447065,0.0002097752,0.009875816],"genre_scores_gemma":[0.9851955,0.0001380481,0.0096863,0.00004437812,0.000005938253,0.00006438371,0.001151208,0.00003496619,0.003679154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1069915,"threshold_uncertainty_score":0.2152432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0266759769706618,"score_gpt":0.2416927509948254,"score_spread":0.2150167740241636,"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."}}