{"id":"W2599676327","doi":"10.1017/aap.2017.2","title":"The Potential and Pitfalls of Large Multi-Source Collections","year":2017,"lang":"en","type":"article","venue":"Advances in Archaeological Practice","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University","funders":"","keywords":"Pottery; Archaeology; TRACE (psycholinguistics); Sample (material); Excavation; Provenance; History; Artifact (error); Computer science; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1607268,0.001481012,0.002109924,0.01050674,0.008074402,0.01012114,0.006048734,0.002199607,0.009517208],"category_scores_gemma":[0.2986648,0.002054821,0.001446601,0.01820575,0.009031167,0.00995661,0.01296821,0.003114737,0.002966573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003358538,"about_ca_system_score_gemma":0.005698524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009083291,"about_ca_topic_score_gemma":0.02396652,"domain_scores_codex":[0.8091246,0.1060153,0.01394272,0.01364115,0.054596,0.002680285],"domain_scores_gemma":[0.5787017,0.2353203,0.03091847,0.08596586,0.06630966,0.002783943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002067965,0.0007561872,0.1938493,0.007467665,0.002139886,0.004096199,0.09922398,0.006120606,0.01543245,0.07306487,0.07144259,0.5243382],"study_design_scores_gemma":[0.0002815008,0.0006053703,0.2285939,0.01019602,0.0009415871,0.006790408,0.1014353,0.009722177,0.01963942,0.1365858,0.4844638,0.000744719],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4315026,0.01637684,0.3830235,0.04352816,0.006081712,0.00643424,0.01205615,0.002419261,0.09857754],"genre_scores_gemma":[0.6026779,0.004525775,0.3579297,0.007736511,0.002058294,0.007647526,0.004205736,0.002164462,0.01105407],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1607268,"threshold_uncertainty_score":0.8500149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01201570670862624,"score_gpt":0.323312765787645,"score_spread":0.3112970590790188,"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."}}