{"id":"W2730070504","doi":"10.1080/iw-6.4.894","title":"High-frequency lake data benefit society through broader engagement with stakeholders: a synthesis of GLEON data use survey and membe rexperiences","year":2016,"lang":"en","type":"article","venue":"Inland Waters","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Ministry of the Environment, Conservation and Parks","funders":"National Natural Science Foundation of China; Global Lake Ecological Observatory Network; University of Wisconsin-Madison; National Science Foundation","keywords":"Outreach; Public engagement; Scope (computer science); Public relations; Citizen science; Community engagement; Environmental resource management; Business; Knowledge management; Political science; Computer science; Environmental science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000683187,0.000150762,0.0002036708,0.00001148441,0.0001841702,0.00003056267,0.0006814879,0.0000470316,0.000173001],"category_scores_gemma":[0.0001277602,0.00008331357,0.00001150808,0.0001037607,0.0005331528,0.001286301,0.001458852,0.00005130669,0.00001376672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002152615,"about_ca_system_score_gemma":0.000005690146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0025908,"about_ca_topic_score_gemma":0.007550096,"domain_scores_codex":[0.9986547,0.00009730274,0.0001888737,0.0005835738,0.0002157791,0.0002597202],"domain_scores_gemma":[0.998587,0.0003386843,0.00009783392,0.0009303685,0.000009586764,0.00003657995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003777179,0.00005127158,0.966808,0.00001734863,0.000148761,0.000003366932,0.001036991,0.00001111332,0.00005334118,0.00002236316,0.03087669,0.0009329887],"study_design_scores_gemma":[0.0004558385,0.00007440388,0.9905463,0.00004284089,0.00005858997,0.00000115657,0.0009063328,0.00001485472,0.0002143848,0.0001116896,0.007383736,0.0001898874],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964436,0.00001905673,0.0004292315,0.001481174,0.00007774525,0.0002230645,0.0007684626,0.00002312527,0.0005345016],"genre_scores_gemma":[0.9949023,0.0007252299,0.003704719,0.0002306586,0.00001079144,0.00002013655,0.0001163027,0.000009436909,0.0002803916],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02373829,"threshold_uncertainty_score":0.421313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1468164139064833,"score_gpt":0.2664409488021505,"score_spread":0.1196245348956673,"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."}}