{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005750955,0.0007227575,0.0004790707,0.007199021,0.001497023,0.001689398,0.001859376,0.001097946,0.04349341],"category_scores_gemma":[0.005331904,0.0005799819,0.0002466502,0.008278109,0.0008561547,0.001857246,0.00590687,0.001180111,0.009478081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002064261,"about_ca_system_score_gemma":0.01109677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01460362,"about_ca_topic_score_gemma":0.01854903,"domain_scores_codex":[0.9975661,0.0009212423,0.0001758218,0.0003425748,0.0004880651,0.0005063377],"domain_scores_gemma":[0.9878011,0.001879514,0.001970518,0.001863294,0.002862607,0.003622992],"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":[0.0005611493,0.0007709426,0.1720324,0.00112401,0.0001143235,0.0003804331,0.002830601,0.002534123,0.008050756,0.01931786,0.2157398,0.5765436],"study_design_scores_gemma":[0.0001928061,0.0003352164,0.2233381,0.0003165265,0.00004747379,0.000427071,0.002989782,0.002173844,0.001072184,0.004498917,0.7645486,0.00005950563],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2461511,0.00777832,0.1790861,0.02426289,0.001171561,0.008574519,0.2394711,0.009305236,0.2841991],"genre_scores_gemma":[0.5642871,0.004909547,0.2262475,0.002434578,0.0008980644,0.00781288,0.1345319,0.0008074398,0.05807099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04349341,"threshold_uncertainty_score":0.1455,"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."}}