{"id":"W1979327584","doi":"10.5268/iw-5.1.566","title":"A Global Lake Ecological Observatory Network (GLEON) for synthesising high–frequency sensor data for validation of deterministic ecological models","year":2015,"lang":"en","type":"article","venue":"Inland Waters","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Business, Innovation and Employment; Global Lake Ecological Observatory Network; Gordon and Betty Moore Foundation; National Science Foundation","keywords":"Cyberinfrastructure; Computer science; Process (computing); Ecology; Environmental science; Ecological network; Data science; Ecosystem","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":[0.007295812,0.0007489027,0.000441082,0.001934847,0.001055317,0.001035259,0.001115483,0.0007913649,0.004105004],"category_scores_gemma":[0.008710898,0.0004468398,0.00063825,0.002379656,0.0006878462,0.002017274,0.002646089,0.0009951176,0.0007200523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002146857,"about_ca_system_score_gemma":0.006882627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05587399,"about_ca_topic_score_gemma":0.09103577,"domain_scores_codex":[0.9978344,0.0006427083,0.0001583178,0.0003202636,0.0009054241,0.0001389147],"domain_scores_gemma":[0.9940491,0.001190322,0.000704071,0.001628627,0.001962699,0.0004651987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001271155,0.001058294,0.1931461,0.0009588405,0.0005273133,0.0005205962,0.001804012,0.2764365,0.0817803,0.02833925,0.09394182,0.3202158],"study_design_scores_gemma":[0.0008071386,0.0007339856,0.1337936,0.0002724764,0.0001547355,0.0001089793,0.0006834898,0.5990351,0.03065818,0.01439244,0.2190958,0.00026408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2408558,0.0005043788,0.6318408,0.002564025,0.0004883248,0.003138937,0.07610534,0.009605027,0.03489735],"genre_scores_gemma":[0.4025446,0.0002640245,0.5129763,0.0002009713,0.00005250279,0.002543971,0.07488189,0.001054514,0.00548139],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05587399,"threshold_uncertainty_score":0.1110975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09548735205038687,"score_gpt":0.2741196933626323,"score_spread":0.1786323413122454,"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."}}