{"id":"W2911414047","doi":"10.1111/gcb.14584","title":"Evaluating ecosystem effects of climate change on tropical island streams using high spatial and temporal resolution sampling regimes","year":2019,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"U.S. Department of Agriculture","keywords":"STREAMS; Climate change; Ecosystem; Sampling (signal processing); Environmental science; Physical geography; Climatology; Geography; Ecology; Oceanography; Geology; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001327919,0.0002131721,0.0002310676,0.0003829746,0.0004172265,0.0003599325,0.000322505,0.000249692,0.0002959018],"category_scores_gemma":[0.00205354,0.0001606091,0.0003059504,0.0006140943,0.0002012871,0.0003591115,0.0004172404,0.000226616,0.00004482712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004245702,"about_ca_system_score_gemma":0.0003856367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02433808,"about_ca_topic_score_gemma":0.07628968,"domain_scores_codex":[0.9993495,0.0003044632,0.00006175565,0.0001346631,0.00008556949,0.00006408621],"domain_scores_gemma":[0.9987429,0.0004186629,0.0003221073,0.0001382771,0.0002413453,0.0001368279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001479154,0.0001153209,0.9773743,0.00002006389,0.0001371338,0.00005808371,0.000164257,0.005592716,0.009636677,0.00004083477,0.00004996329,0.006662752],"study_design_scores_gemma":[0.00000640817,0.0001124614,0.9858611,0.000002426498,0.00003177271,0.00002751559,0.0001566814,0.01257012,0.001103678,0.0000247664,0.00009639937,0.000006725074],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999166,0.00001483359,0.0005674361,0.000006728286,8.722079e-7,0.00001166529,0.0001070315,0.000005343716,0.0001200991],"genre_scores_gemma":[0.9963058,0.00001964545,0.003243351,0.000009038852,0.000002475108,0.00003708425,0.0003334538,0.000002332156,0.00004678835],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02433808,"threshold_uncertainty_score":0.04839283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05353582780194616,"score_gpt":0.3107959735302372,"score_spread":0.2572601457282911,"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."}}