{"id":"W2121328023","doi":"10.1007/s13253-010-0040-8","title":"Managing the Essential Zeros in Quantitative Fatty Acid Signature Analysis","year":2010,"lang":"en","type":"article","venue":"Journal of Agricultural Biological and Environmental Statistics","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University; University of New Brunswick","funders":"","keywords":"Statistical inference; Inference; Point estimation; Statistics; Sample size determination; Logarithm; Population; Confidence interval; Sample (material); Mathematics; Computer science; Econometrics; Artificial intelligence","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":[0.0002118253,0.000106699,0.000182873,0.00002598749,0.0001066167,0.00005760076,0.0003164983,0.00008094058,0.00006278013],"category_scores_gemma":[0.00006572322,0.00004492723,0.00006853438,0.0001805623,0.0001576355,0.0001083892,0.0001533689,0.0004405393,0.000002370753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001194569,"about_ca_system_score_gemma":0.000002654434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003537501,"about_ca_topic_score_gemma":0.000008158599,"domain_scores_codex":[0.9992539,0.0000587108,0.0002542347,0.0001437204,0.0001396885,0.000149741],"domain_scores_gemma":[0.9995,0.0001541306,0.000196112,0.00007144059,0.00001606769,0.00006227817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003502115,0.0001838183,0.05547433,0.000008147192,0.0002436789,0.0001346198,0.0008681834,0.001572169,0.9224207,0.009813016,0.0004123423,0.008833975],"study_design_scores_gemma":[0.0001988895,0.0001685438,0.9829338,0.000004570823,0.00004896127,0.0001135541,0.000718036,0.002030191,0.006476503,0.006791476,0.0003776287,0.0001378754],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9290674,0.0001734235,0.06849103,0.001962368,0.0001167848,0.00005073619,0.00002644858,0.000003850912,0.0001079625],"genre_scores_gemma":[0.9771865,0.0001803523,0.0224736,0.0000693183,0.00004036907,8.32066e-7,0.00001530666,4.255993e-7,0.00003330902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9274594,"threshold_uncertainty_score":0.1913949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008164959991097731,"score_gpt":0.2047902359506426,"score_spread":0.1966252759595449,"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."}}