{"id":"W4205366521","doi":"10.1016/j.jenvman.2022.114515","title":"Improving litterfall production prediction in China under variable environmental conditions using machine learning algorithms","year":2022,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Natural Resources and Forestry; Ontario Forest Research Institute; University of British Columbia","funders":"","keywords":"Random forest; Plant litter; Primary production; Abiotic component; Environmental science; Production (economics); Ecosystem; Ecology; Machine learning; Computer science; Biology","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":[0.0009581393,0.0008140842,0.0008252654,0.00137156,0.0005578386,0.0006198792,0.0008695804,0.0007227917,0.0005659264],"category_scores_gemma":[0.001189378,0.0003233098,0.0007872772,0.0009620269,0.0002393895,0.0007485296,0.0003570215,0.0002860831,0.000115401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001218692,"about_ca_system_score_gemma":0.001692294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07760272,"about_ca_topic_score_gemma":0.05975794,"domain_scores_codex":[0.999727,0.00003691367,0.00002059986,0.0001115859,0.00004516731,0.0000586959],"domain_scores_gemma":[0.9992104,0.0003331873,0.00009165517,0.00004910867,0.0002563927,0.00005942082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003247873,0.0003363759,0.1948071,0.0001158961,0.0001943231,0.0003150146,0.00006968937,0.708887,0.003999156,0.0003826241,0.001427244,0.08914079],"study_design_scores_gemma":[0.000006670084,0.00001197777,0.01190236,0.000001913517,0.00001733212,0.000005356175,0.000009078878,0.9874773,0.0004127863,0.00009772299,0.00005285251,0.000004713574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9833941,0.0002940347,0.01515985,0.000101208,0.00003142145,0.0000138655,0.0002635934,0.0001826312,0.0005591544],"genre_scores_gemma":[0.9958782,0.00007723487,0.00312544,0.00001636263,0.00001728287,0.00001077,0.000412841,0.000009339376,0.0004524569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07760272,"threshold_uncertainty_score":0.1543021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004774922561420528,"score_gpt":0.1849169422098116,"score_spread":0.1801420196483911,"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."}}