{"id":"W2912200409","doi":"10.5194/isprs-archives-xlii-2-w9-305-2019","title":"RECREATING CULTURAL HERITAGE ENVIRONMENTS FOR VR USING PHOTOGRAMMETRY","year":2019,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Photogrammetry; Cultural heritage; Workflow; Computer science; Virtual reality; 3d model; Motion (physics); Space (punctuation); Computer graphics (images); Computer vision; Geography; Human–computer interaction; Artificial intelligence; Archaeology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004928657,0.0005568723,0.0002521764,0.001090522,0.0007709644,0.002275974,0.0007414087,0.0005089627,0.008133109],"category_scores_gemma":[0.0007302755,0.0003757259,0.0008658775,0.0006418298,0.0006075653,0.0008794013,0.002168889,0.0006395915,0.002863762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004569645,"about_ca_system_score_gemma":0.0006007024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003990333,"about_ca_topic_score_gemma":0.00723565,"domain_scores_codex":[0.9996101,0.0001410933,0.00002044445,0.00007491031,0.0001156323,0.00003776086],"domain_scores_gemma":[0.9996833,0.00006983919,0.00002790568,0.0001476168,0.00004128722,0.00003013496],"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":[0.0001801699,0.0002709969,0.004335656,0.000858128,0.0001060883,0.001725116,0.009251525,0.09085481,0.1229796,0.05410735,0.01559407,0.6997365],"study_design_scores_gemma":[0.0001199722,0.0003175105,0.01641258,0.0007073857,0.0001173931,0.002960632,0.009790746,0.2688511,0.08182833,0.04977992,0.5687478,0.0003665755],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02737758,0.0001766311,0.9454747,0.0002888652,0.0000544159,0.0002320111,0.0004308815,0.004573758,0.02139117],"genre_scores_gemma":[0.2715393,0.0004686516,0.7155519,0.00009346984,0.00001863438,0.0002218811,0.0007111729,0.0006099546,0.01078503],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008133109,"threshold_uncertainty_score":0.02720797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02420193832997715,"score_gpt":0.2514406466647666,"score_spread":0.2272387083347894,"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."}}