{"id":"W2005839156","doi":"10.4028/www.scientific.net/amm.120.168","title":"Face Data Acquisition and Error Analysis","year":2011,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Advanced Measurement and Detection Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Data collection; Merge (version control); Computer science; Point cloud; Data acquisition; Process (computing); Data processing; Point (geometry); Artificial intelligence; Statistics; Mathematics; Information retrieval; Database","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":[0.001302225,0.0007546612,0.0008153427,0.002492248,0.0007916899,0.001054578,0.0009593331,0.0007575798,0.006361011],"category_scores_gemma":[0.005460228,0.0002734603,0.0004515759,0.001497214,0.0003365671,0.001051252,0.001168391,0.0007573134,0.002505205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000434405,"about_ca_system_score_gemma":0.0008348273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004413754,"about_ca_topic_score_gemma":0.003072672,"domain_scores_codex":[0.9975611,0.0001834998,0.000113432,0.0003613264,0.001637926,0.0001426993],"domain_scores_gemma":[0.9965847,0.0004082335,0.0001643939,0.0005749103,0.00220196,0.00006577805],"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":[0.0007377394,0.0001915604,0.02441772,0.0004601754,0.0001040951,0.0003961514,0.0007581705,0.01146012,0.1001964,0.003471293,0.01412331,0.8436833],"study_design_scores_gemma":[0.00005651968,0.0005041257,0.1101985,0.0001627764,0.0001545101,0.002557098,0.001184025,0.2868771,0.5209341,0.00535774,0.07171119,0.0003022067],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1434896,0.0005205876,0.8303294,0.0004366369,0.0003577675,0.0008458876,0.005356215,0.005651046,0.01301302],"genre_scores_gemma":[0.3605958,0.0006577524,0.6181784,0.0002196015,0.0001513964,0.00108672,0.005161899,0.001082979,0.01286555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006361011,"threshold_uncertainty_score":0.02127969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08016290221050777,"score_gpt":0.2705234004710373,"score_spread":0.1903604982605295,"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."}}