{"id":"W2612497353","doi":"10.1111/conl.12359","title":"Unsupported Conclusions on Net Conservation Benefits of Mislabeling Seafood","year":2017,"lang":"en","type":"article","venue":"Conservation Letters","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"IUCN Red List; Ranking (information retrieval); Conservation status; Vagueness; Fishery; Biology; Geography; Statistics; Ecology; Computer science; Mathematics; Information retrieval; 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":[0.0002638048,0.000128514,0.0001311792,0.00007114608,0.000452283,0.00007549228,0.0003297424,0.0001076485,0.00004120056],"category_scores_gemma":[0.0004964618,0.0001406355,0.00006914956,0.00006412729,0.0001906709,0.00001676781,0.00006604836,0.00007227548,0.00003324303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001139723,"about_ca_system_score_gemma":0.00006468243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005409849,"about_ca_topic_score_gemma":0.00003620929,"domain_scores_codex":[0.9989307,0.00005673497,0.0003731541,0.0003116213,0.0001839697,0.0001438206],"domain_scores_gemma":[0.9981858,0.00003352305,0.000520866,0.000880869,0.0003259811,0.00005298461],"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":[0.00006097743,0.00006718728,0.02426751,0.00002159126,0.00005154244,3.557159e-7,0.00006748362,0.000124019,0.9181479,0.004351222,0.05208187,0.0007584118],"study_design_scores_gemma":[0.001278624,0.0001376105,0.3397361,0.00006927952,0.00004128692,0.00000465929,0.00008031464,0.0006268619,0.5871488,0.00004196429,0.07052171,0.0003127139],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9664094,0.00003320207,0.001625936,0.030541,0.0004241068,0.0002538231,0.00006076031,0.00002114693,0.0006306582],"genre_scores_gemma":[0.989231,0.00003495909,0.0005282299,0.008705438,0.0001346655,0.00002968179,0.0006285502,0.00001853097,0.0006889589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.330999,"threshold_uncertainty_score":0.573495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04282669357567789,"score_gpt":0.2887625676340864,"score_spread":0.2459358740584085,"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."}}