{"id":"W4243248165","doi":"10.5194/isprs-archives-xlii-2-w15-1163-2019","title":"THE DIGITAL RESTITUTION OF LOT 3317: USING UNDERWATER IMAGE BASEDMODELLING TO GENERATE VALUE IN VIRTUAL HERITAGE EXPERIENCES","year":2019,"lang":"en","type":"article","venue":"The international 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":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Computer graphics (images); Value (mathematics); Image (mathematics); Pipeline (software); Polygon (computer graphics); Cultural heritage; Heuristic; Polygon mesh; Artificial intelligence; Computer vision; Geography; Telecommunications; Frame (networking); Archaeology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.0003438903,0.0003040298,0.0001348794,0.0002421446,0.0002959741,0.001047386,0.0007075132,0.0003367458,0.004285814],"category_scores_gemma":[0.0008580969,0.0001944201,0.0003106407,0.0001872798,0.0006100502,0.0005784927,0.001332684,0.0003193524,0.0002901061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006034482,"about_ca_system_score_gemma":0.0004773358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005569547,"about_ca_topic_score_gemma":0.0102755,"domain_scores_codex":[0.9998658,0.00004172526,0.000003784925,0.00001496384,0.00005482789,0.00001891539],"domain_scores_gemma":[0.999752,0.0001030424,0.0000194267,0.00006098528,0.00003358158,0.00003092919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006730165,0.0003698128,0.006404835,0.0004384722,0.00005489783,0.001364087,0.00446567,0.6578198,0.1260927,0.02344972,0.003847228,0.1750198],"study_design_scores_gemma":[0.00004416968,0.0003992273,0.004053729,0.00007026126,0.00003321107,0.0002651384,0.001145007,0.9370873,0.03389433,0.005360449,0.01758087,0.00006623798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7274604,0.00007557576,0.2454891,0.0002889199,0.00003783355,0.0001891679,0.000249419,0.0009596786,0.02524992],"genre_scores_gemma":[0.9347599,0.00004334228,0.06069846,0.0000230745,0.00000203377,0.00005103016,0.0001360678,0.0001097812,0.004176324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005569547,"threshold_uncertainty_score":0.01433748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01969713359599179,"score_gpt":0.2388002410053482,"score_spread":0.2191031074093564,"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."}}