{"id":"W4405249241","doi":"10.1017/pab.2024.43","title":"The category-modifier system: a hierarchical classification scheme for vertebrate tooth marks","year":2024,"lang":"en","type":"article","venue":"Paleobiology","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Dinosaur Research Institute; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"sort; Range (aeronautics); Vertebrate; Classification scheme; Computer science; Evolutionary biology; Artificial intelligence; Biology; Data science; Information retrieval; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004765539,0.00008615451,0.00008783623,0.00001708363,0.0002829982,0.00003239062,0.0001793662,0.0001509063,0.0001625236],"category_scores_gemma":[0.00006783168,0.00005715877,0.00004924139,0.0001018913,0.000275304,0.00006414238,0.00004625595,0.000140739,0.000772987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008184208,"about_ca_system_score_gemma":0.00002666639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001884214,"about_ca_topic_score_gemma":0.0001948508,"domain_scores_codex":[0.9991375,0.0001191863,0.0001721131,0.0002819592,0.00004737903,0.0002419044],"domain_scores_gemma":[0.999316,0.0004268678,0.00003305679,0.0001803984,0.000007128378,0.000036538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000268227,0.0000653907,0.4777105,0.0001173541,0.0001265522,0.00001199121,0.0004329019,0.0000425244,0.0159427,0.3666261,0.09867042,0.0399853],"study_design_scores_gemma":[0.0003439363,0.0001795903,0.6337436,0.00001747992,0.00003895256,0.00004236373,0.0002046421,0.1218574,0.0001520849,0.008809598,0.2343693,0.0002409698],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9556754,0.0004244646,0.007929149,0.01207299,0.001792349,0.0007462543,0.00001793692,0.0002020807,0.02113938],"genre_scores_gemma":[0.9969895,0.00002061652,0.0002700553,0.0003928797,0.0001070569,0.0003214225,0.00003620076,0.000008024586,0.001854277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3578165,"threshold_uncertainty_score":0.9935441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01745890049855624,"score_gpt":0.2475169164870797,"score_spread":0.2300580159885234,"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."}}