{"id":"W3043975290","doi":"10.1016/j.jprot.2020.103920","title":"Shell palaeoproteomics: First application of peptide mass fingerprinting for the rapid identification of mollusc shells in archaeology","year":2020,"lang":"en","type":"article","venue":"Journal of Proteomics","topic":"Forensic Anthropology and Bioarchaeology Studies","field":"Arts and Humanities","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Campus France; Université de Bourgogne; Università di Pisa; Ministero dell’Istruzione, dell’Università e della Ricerca; Providence Health Care; Università Italo Francese; University of York","keywords":"Taxon; Phylogenetic tree; Peptide mass fingerprinting; Shell (structure); Identification (biology); Archaeology; Prehistory; Biology; Paleontology; Evolutionary biology; Geography; Ecology; Materials science; Biochemistry; Proteomics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0008027379,0.0001042292,0.0003594887,0.00009546497,0.000123823,0.000005677894,0.0002914561,0.00007493889,0.00004524631],"category_scores_gemma":[0.0003099504,0.00007311148,0.0001469872,0.00005307416,0.004007615,0.00008081332,0.00007707059,0.0002319282,0.000002036741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002049658,"about_ca_system_score_gemma":0.00004844679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001709074,"about_ca_topic_score_gemma":0.0008015857,"domain_scores_codex":[0.9986809,0.00005272281,0.0008909337,0.0001228846,0.0001018851,0.0001506394],"domain_scores_gemma":[0.998056,0.0003265202,0.001152865,0.0001206252,0.0003207637,0.00002324383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003675609,0.0003774729,0.07243439,0.001665176,0.00106825,0.00001071521,0.2189807,0.003771217,0.08159563,0.5976902,0.001061512,0.01766917],"study_design_scores_gemma":[0.009858172,0.006066055,0.02688272,0.0005977722,0.0009810915,0.00008440715,0.1771652,0.05601538,0.5662343,0.1240716,0.03082426,0.001219132],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.732318,0.001696299,0.1947694,0.0674132,0.0008388053,0.002444615,0.0001065623,0.00001590732,0.0003972283],"genre_scores_gemma":[0.9926915,0.0003712287,0.006376525,0.0001360591,0.0003407847,0.0000338291,0.000002651708,0.000009858309,0.00003761464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4846386,"threshold_uncertainty_score":0.9987029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02741187153425976,"score_gpt":0.2432848047148196,"score_spread":0.2158729331805599,"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."}}