{"id":"W7132199108","doi":"","title":"NRC 3D Imaging Technology for Museums & Heritage","year":2002,"lang":"en","type":"article","venue":"NPARC","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Cultural heritage; Variety (cybernetics); National heritage; Research council; Industrial heritage; High resolution","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.001558252,0.0007962518,0.0006862133,0.004064283,0.002497052,0.002724876,0.001576824,0.001428561,0.05641137],"category_scores_gemma":[0.002186472,0.0006560855,0.0006621529,0.004230063,0.0008032573,0.001253278,0.001943428,0.001863524,0.0221039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005148865,"about_ca_system_score_gemma":0.01032407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1908097,"about_ca_topic_score_gemma":0.314597,"domain_scores_codex":[0.9968924,0.0001185185,0.00005262679,0.0001491169,0.002660894,0.0001264258],"domain_scores_gemma":[0.996878,0.0001483933,0.0000624192,0.00041823,0.002313139,0.0001797333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001189696,0.0000366433,0.0009548254,0.0005086859,0.00001685738,0.0003065717,0.0003798573,0.001288443,0.03190948,0.02810421,0.4231106,0.5132649],"study_design_scores_gemma":[0.000009457677,0.0000140677,0.001496249,0.00009767351,0.00001011032,0.0004161946,0.0000417816,0.001267271,0.00671365,0.0009778854,0.988922,0.00003367654],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.006073834,0.01600681,0.2997183,0.004197157,0.00233655,0.0007726242,0.01341027,0.01788139,0.639603],"genre_scores_gemma":[0.04789161,0.01567765,0.3768299,0.00168933,0.0003168678,0.0007416157,0.01904089,0.003445566,0.5343666],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1908097,"threshold_uncertainty_score":0.379398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02078726150133403,"score_gpt":0.2065581786021954,"score_spread":0.1857709171008614,"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."}}