{"id":"W2386894261","doi":"","title":"[Prediction of litter moisture content in Tahe Forestry Bureau of Northeast China based on FWI moisture codes].","year":2014,"lang":"en","type":"article","venue":"PubMed","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Water content; Moisture; Environmental science; Litter; Forestry; Meteorology; Hydrology (agriculture); Geology; Geography; Waste management; Engineering; Geotechnical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000270701,0.0003332429,0.0001553432,0.001106629,0.0002266177,0.0002402298,0.0002694974,0.0001983776,0.0007251982],"category_scores_gemma":[0.000561785,0.0001263402,0.0003262121,0.000791228,0.00009606705,0.0003370845,0.0001744897,0.0001344113,0.0001701462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006375296,"about_ca_system_score_gemma":0.0009048846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1681819,"about_ca_topic_score_gemma":0.1712278,"domain_scores_codex":[0.9999281,0.000007552355,0.000007960295,0.00001878737,0.00002557918,0.00001196448],"domain_scores_gemma":[0.9998224,0.00002833278,0.00004402359,0.000009928701,0.00007077034,0.00002460963],"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":[0.0001066872,0.00005252175,0.8852949,0.0002626759,0.00009802478,0.00027324,0.0001475254,0.02289747,0.010364,0.0002026514,0.003012917,0.07728736],"study_design_scores_gemma":[0.00001722431,0.00002811768,0.8874211,0.00002108041,0.00004666312,0.00005075233,0.0001208719,0.1070679,0.003224564,0.0001125811,0.001872738,0.00001633602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885973,0.0003448112,0.004118728,0.00009532121,0.00002668422,0.00004647725,0.005287377,0.0001995163,0.001283894],"genre_scores_gemma":[0.9910416,0.000222032,0.003591811,0.00001737961,0.000008307001,0.00002397291,0.004532475,0.000007810138,0.0005546921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1681819,"threshold_uncertainty_score":0.3344059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02871075171725841,"score_gpt":0.191793992041775,"score_spread":0.1630832403245166,"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."}}