{"id":"W2153979575","doi":"10.1016/j.anifeedsci.2003.08.006","title":"Near-infrared evaluation of wet mink diets","year":2003,"lang":"en","type":"article","venue":"Animal Feed Science and Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Nova Scotia Department of Agriculture","funders":"University of Pittsburgh","keywords":"Calibration; Mink; Population; Partial least squares regression; Standard error; Mathematics; Animal science; Analytical Chemistry (journal); Biology; Chemistry; Statistics; Chromatography; Ecology","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":[0.0005378805,0.0000785734,0.0001447824,0.0002636191,0.0001773889,0.00002431815,0.0002270095,0.0001217533,0.0003755809],"category_scores_gemma":[0.001184785,0.00007102187,0.00001772222,0.002176456,0.001061184,0.0001163046,0.00005121081,0.000104072,0.000006706983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004948893,"about_ca_system_score_gemma":0.0002518091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001061995,"about_ca_topic_score_gemma":0.000002639766,"domain_scores_codex":[0.9989508,0.00000637532,0.000140573,0.0002668313,0.0004101694,0.0002252715],"domain_scores_gemma":[0.9992551,0.00002458839,0.0000764951,0.0002199053,0.0003841576,0.00003976782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006164034,0.00003951111,0.02117494,0.0000103321,0.00001199937,0.000001021597,0.00004197118,6.051733e-7,0.9676621,0.008392488,0.00008008247,0.002578804],"study_design_scores_gemma":[0.0002769389,0.00008602192,0.00286915,0.000005197508,0.00007416847,0.00001584322,0.0006034506,0.0007476799,0.9891444,0.005322512,0.0007665355,0.00008812375],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9520015,0.0007175504,0.00001034638,0.0002059811,0.00001390588,0.00003149273,0.000001644089,0.00004663346,0.046971],"genre_scores_gemma":[0.9992852,0.00002517086,0.0004481883,0.00001679523,0.000005244699,0.000007367996,6.431629e-7,0.000003611041,0.0002078175],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04728372,"threshold_uncertainty_score":0.4112347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02381396525194909,"score_gpt":0.3044802505367151,"score_spread":0.280666285284766,"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."}}