{"id":"W2947665348","doi":"10.1111/gcb.14717","title":"Disentangling how climate change can affect an aquatic food web by combining multiple experimental approaches","year":2019,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Animal and Plant Science Education","field":"Psychology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Climate change; Abiotic component; Ecology; Ecosystem; Stressor; Food web; Environmental science; Aquatic ecosystem; Biology; Environmental change","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001474824,0.0007725378,0.0009816136,0.0006712951,0.001028308,0.0008658544,0.001121333,0.0007733215,0.001994753],"category_scores_gemma":[0.001170281,0.0004755784,0.001148552,0.0004833323,0.001438024,0.001284587,0.002063337,0.002855958,0.0002432528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009139761,"about_ca_system_score_gemma":0.000891018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00171171,"about_ca_topic_score_gemma":0.004844607,"domain_scores_codex":[0.9982073,0.0003481207,0.0002813201,0.0006053243,0.0003290468,0.0002289107],"domain_scores_gemma":[0.9975377,0.0006930467,0.0004749224,0.0006469093,0.0002268622,0.0004204964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001796537,0.002645311,0.01250424,0.0005556357,0.0004968864,0.0001437838,0.0005213232,0.0008036515,0.965124,0.001075668,0.0002531685,0.01407982],"study_design_scores_gemma":[0.001246816,0.032885,0.3121548,0.0002068561,0.002448702,0.0005577817,0.001404505,0.01482068,0.6044692,0.008142705,0.02121181,0.000451144],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9711809,0.0007334238,0.02276431,0.0002813798,0.0002733322,0.0008679279,0.0007031531,0.0001797598,0.003015841],"genre_scores_gemma":[0.9360412,0.000716112,0.05116514,0.001235218,0.0001600402,0.006887889,0.001005867,0.0002714622,0.002517077],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001994753,"threshold_uncertainty_score":0.007799745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1752127426588254,"score_gpt":0.3399054350281003,"score_spread":0.1646926923692749,"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."}}