{"id":"W2293854577","doi":"10.1093/biosci/biv174","title":"Biological Field Stations: A Global Infrastructure for Research, Education, and Public Engagement","year":2016,"lang":"en","type":"article","venue":"BioScience","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Global Lake Ecological Observatory Network","keywords":"Biome; Situated; Relevance (law); Environmental resource management; Cover (algebra); Field (mathematics); Environmental research; Citizen science; Environmental planning; Environmental education; Geography; Political science; Environmental science; Ecology; Ecosystem; Computer science; Engineering; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004095981,0.0000485975,0.00003854749,0.00001691256,0.00029356,0.00005921878,0.0001942613,0.000035748,0.006870697],"category_scores_gemma":[0.0008725366,0.00002896119,0.00001285903,0.0003016362,0.0004224717,0.0001623475,0.0001743341,0.00003005236,0.00008227502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002953264,"about_ca_system_score_gemma":0.00004348587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003996816,"about_ca_topic_score_gemma":0.00006519398,"domain_scores_codex":[0.999208,0.00003755349,0.00007769207,0.0002434132,0.0001838771,0.0002494652],"domain_scores_gemma":[0.9996043,0.0001143516,0.00002255567,0.0001153844,0.00003237727,0.0001110608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001496141,0.0001814145,0.390384,0.000006854949,0.000002270747,3.426423e-7,0.0001910228,1.437369e-7,0.03955037,0.07802995,0.2766254,0.2150133],"study_design_scores_gemma":[0.0001513024,0.0001361042,0.6662021,0.000005581266,7.83969e-7,0.000002620643,0.001729129,0.000008522835,0.0007283225,0.007099294,0.3238578,0.00007848609],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9750429,0.0000407094,0.003892318,0.01164707,0.0001963745,0.0003148355,0.0001050626,0.00002381641,0.008736942],"genre_scores_gemma":[0.9981328,0.00009388269,0.0007110198,0.000730938,0.00001956005,0.00007505423,0.000004539818,0.000001176411,0.0002309824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.275818,"threshold_uncertainty_score":0.9940372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1416505064189638,"score_gpt":0.3881502048824919,"score_spread":0.2464996984635281,"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."}}