{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003555322,0.0001123444,0.0002203817,0.00008803028,0.00003040717,0.00001185059,0.0001033282,0.0001043024,0.00003273271],"category_scores_gemma":[0.00009417063,0.00007704646,0.0000658321,0.0001222885,0.00005757182,0.00003743486,0.000003443135,0.0001650822,0.00000468974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004396715,"about_ca_system_score_gemma":0.00001203515,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00808538,"about_ca_topic_score_gemma":0.00405214,"domain_scores_codex":[0.9990245,0.00009077259,0.0002418278,0.0001777511,0.0002408017,0.0002243656],"domain_scores_gemma":[0.9994755,0.00008258325,0.0001080343,0.0002126198,0.00003261301,0.00008865603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001725238,0.00004140846,0.9047543,0.00003780922,0.000008867247,0.000002734306,0.00009371054,0.006659302,0.000006493396,0.00001301855,0.000337563,0.0878723],"study_design_scores_gemma":[0.0005702613,0.00008568123,0.9726359,0.00003662671,0.00001138194,0.000002618971,0.00002435337,0.02531408,0.00008597036,0.0001310905,0.001033825,0.00006824871],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816471,0.00006009327,0.0000739976,0.0004755385,0.0002606941,0.0002505284,0.00009268426,0.00001905301,0.01712035],"genre_scores_gemma":[0.9992623,0.000007873762,0.0000661253,0.0002264866,0.0001087475,0.000001861735,0.000162172,0.000003597995,0.0001608704],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08780406,"threshold_uncertainty_score":0.9985198,"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."}}