{"id":"W2748389517","doi":"10.5194/isprs-archives-xlii-2-w5-171-2017","title":"LEVERAGING EXISTING HERITAGE DOCUMENTATION FOR ANIMATIONS: SENATE VIRTUAL TOUR","year":2017,"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":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Documentation; Workflow; Computer science; World Wide Web; Leverage (statistics); Point (geometry); Technical documentation; Multimedia; Database","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":[0.001055178,0.0007946819,0.0001995822,0.0009388604,0.00223759,0.002166747,0.0006487417,0.0004207364,0.02786103],"category_scores_gemma":[0.001947121,0.000356041,0.0005206413,0.001212737,0.0007149176,0.0008648941,0.002914002,0.0007599226,0.003682231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009640877,"about_ca_system_score_gemma":0.0016992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02643099,"about_ca_topic_score_gemma":0.1113972,"domain_scores_codex":[0.9996408,0.0001779204,0.00001017973,0.00003657773,0.00006499917,0.00006958507],"domain_scores_gemma":[0.9989676,0.0003341512,0.00003545897,0.0002445719,0.0001847138,0.0002334242],"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":[0.003216331,0.001263631,0.02492452,0.001409316,0.0001818774,0.003956075,0.05623028,0.05731007,0.02745666,0.01763468,0.2702721,0.5361444],"study_design_scores_gemma":[0.0003165398,0.0008109649,0.03737576,0.0007820621,0.0001286682,0.0009363919,0.03577596,0.05938046,0.01696102,0.004707677,0.8424451,0.0003793829],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6405421,0.0003602434,0.05018645,0.002140038,0.0003889583,0.001236332,0.01384996,0.01272974,0.2785661],"genre_scores_gemma":[0.8031654,0.0004962479,0.1236783,0.0002108901,0.00005159318,0.0009238175,0.0100179,0.002863916,0.05859196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02786103,"threshold_uncertainty_score":0.09320444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03596283973916537,"score_gpt":0.2783876481035686,"score_spread":0.2424248083644033,"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."}}