{"id":"W2067145094","doi":"10.1081/css-200043187","title":"Compositional Analysis of Cattle Manure During Composting Using a Field‐Portable Near‐Infrared Spectrometer","year":2005,"lang":"en","type":"article","venue":"Communications in Soil Science and Plant Analysis","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":129,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Manure; Compost; Stockpile; Environmental science; Feedlot; Raw material; Straw; Nutrient; Pulp and paper industry; Composition (language); Biogas; Waste management; Chemistry; Agronomy; Animal science; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0004208862,0.0001187888,0.0004380612,0.001563938,0.0005586238,0.0001026759,0.0007741799,0.00006152338,0.0005585987],"category_scores_gemma":[0.0000965612,0.0001189294,0.0001620513,0.009992503,0.0004496207,0.000253052,0.0002541133,0.0001978593,0.00000136519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001487793,"about_ca_system_score_gemma":0.00009365688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001141471,"about_ca_topic_score_gemma":0.001654376,"domain_scores_codex":[0.9986571,0.00002359583,0.0004227372,0.0002841062,0.0003595337,0.0002529243],"domain_scores_gemma":[0.9983888,0.0003062721,0.0002170434,0.0008678696,0.0001458277,0.00007413499],"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":[0.00002479939,0.0002846492,0.8025844,0.00003259958,0.002684045,0.000002606605,0.001113608,0.02339078,0.1691327,0.0002951385,0.000026272,0.0004284144],"study_design_scores_gemma":[0.000322184,0.00001248012,0.1268174,0.00003541553,0.009157309,0.00001236614,0.001150444,0.7521986,0.1097338,0.00007247352,0.0001340496,0.0003534717],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919002,0.0007016278,0.0002298179,0.0001961105,0.000003915307,0.00002126238,0.00007168174,0.0000160712,0.006859278],"genre_scores_gemma":[0.9903936,0.0002239569,0.009034797,0.00004429695,0.0000120478,0.000004028965,0.0001584208,0.000003611352,0.0001252709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7288078,"threshold_uncertainty_score":0.6116264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03277086102974354,"score_gpt":0.3158700235043078,"score_spread":0.2830991624745643,"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."}}