{"id":"W1987263018","doi":"10.1111/avsc.12143","title":"Climatic characterization of forest zones across administrative boundaries improves conservation planning","year":2014,"lang":"en","type":"article","venue":"Applied Vegetation Science","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia; Ministry of Forests","funders":"U.S. Forest Service","keywords":"Baseline (sea); Geography; Climate change; Ecosystem; Vegetation (pathology); Forest ecology; Ecosystem services; Physical geography; Ecology; Environmental science; Geology; Oceanography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006209185,0.0001048773,0.0001277355,0.00003363876,0.0005540501,0.0002013502,0.0002211692,0.00003797367,0.0003561286],"category_scores_gemma":[0.0001345399,0.00009816491,0.00002196837,0.0005988753,0.001709643,0.0005562315,0.00008843485,0.00004972319,0.0001527084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001263424,"about_ca_system_score_gemma":0.00003810664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002694068,"about_ca_topic_score_gemma":0.00009107152,"domain_scores_codex":[0.9987014,0.00001827741,0.0002891897,0.0002785435,0.0004657423,0.0002468239],"domain_scores_gemma":[0.9993492,0.00006101932,0.0002980855,0.0001719684,0.00005288903,0.00006685956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00002248484,0.0000456422,0.03854506,0.00003291281,0.000002349761,1.419895e-7,0.004196663,0.00005983335,0.9026281,0.05146938,0.00002481221,0.002972641],"study_design_scores_gemma":[0.0002212122,0.00005780894,0.9383383,0.00001219094,0.000005018022,0.000001291913,0.001685853,0.0025457,0.05456095,0.001006395,0.001440774,0.0001245515],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9729452,0.000002174728,0.01358516,0.0001827833,0.0001162877,0.000206754,0.00001117463,0.00003756438,0.01291294],"genre_scores_gemma":[0.9990867,0.000001879918,0.0004439717,0.0002983609,0.00001322606,0.00003891743,0.00007259108,0.000005449755,0.0000388527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8997932,"threshold_uncertainty_score":0.6299249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03545985856230852,"score_gpt":0.3042384538364832,"score_spread":0.2687785952741747,"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."}}