{"id":"W2980239312","doi":"10.1126/science.aaw3372","title":"Global modeling of nature’s contributions to people","year":2019,"lang":"en","type":"article","venue":"Science","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":459,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada); McGill University","funders":"Marcus och Amalia Wallenbergs minnesfond; Deutsche Forschungsgemeinschaft","keywords":"Pace; Climate change; Natural resource economics; Sustainable development; Face (sociological concept); Pollination; Development economics; Environmental planning; Scale (ratio); Environmental resource management; Geography; Global warming; Environmental protection; Ecology; Environmental science; Economics; Biology; Sociology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003206159,0.00004228638,0.00007344321,0.00001574038,0.00008411976,0.00002056515,0.0003823096,0.00002752817,0.0003957247],"category_scores_gemma":[0.00002884792,0.00003177951,0.00001968023,0.0007701948,0.00001755271,0.0002578764,0.0002030472,0.00003109877,0.000781385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001071632,"about_ca_system_score_gemma":0.00002649422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005333091,"about_ca_topic_score_gemma":0.001421982,"domain_scores_codex":[0.9991906,0.000005585372,0.00009623312,0.0001989059,0.0003062058,0.000202523],"domain_scores_gemma":[0.9996602,0.000006769826,0.00002479276,0.0001954897,0.00002144737,0.00009128286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008024741,0.0000324344,0.7797641,0.00001113506,0.000001821334,3.868029e-7,0.0002668064,0.2092103,0.008712164,0.001634006,0.00009680201,0.0002620232],"study_design_scores_gemma":[0.0002647564,0.00008978479,0.4166577,0.00003849519,0.000006458342,0.000006196108,0.0002101697,0.5752002,0.003098741,0.002498673,0.001709746,0.0002190189],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988583,0.00001963383,0.0004334475,0.0002472441,0.0002243853,0.0001107253,0.00002892527,0.00001228444,0.01034035],"genre_scores_gemma":[0.9996158,0.000001071701,0.0001919043,0.0001550266,0.000009525993,0.000002200645,6.133814e-7,0.000001112964,0.00002271132],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3659899,"threshold_uncertainty_score":0.9999966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003695374351549715,"score_gpt":0.2441086555175574,"score_spread":0.2404132811660077,"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."}}