{"id":"W2566082849","doi":"10.1109/crv.2016.17","title":"Registration of Modern and Historic Imagery for Timescape Creation","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Digitization; Computer science; Cultural heritage; Scope (computer science); Computer vision; Artificial intelligence; Visualization; Computer graphics (images); Image registration; Image (mathematics); Geography; Archaeology","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":[0.0005193229,0.0004992563,0.000312968,0.001769192,0.0005028354,0.001977691,0.0005630807,0.0004177689,0.01555349],"category_scores_gemma":[0.00169917,0.0003723382,0.0004780913,0.00188985,0.0003530379,0.001611314,0.001366204,0.0007953274,0.00499732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002977736,"about_ca_system_score_gemma":0.0004972775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009584182,"about_ca_topic_score_gemma":0.001746275,"domain_scores_codex":[0.9996759,0.00005250586,0.00001717343,0.00005779613,0.000160759,0.00003596691],"domain_scores_gemma":[0.999522,0.00007079402,0.00004140988,0.0001756152,0.0001410564,0.00004908325],"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":[0.0005788996,0.0001199679,0.001687635,0.0006502211,0.00006798378,0.0006516092,0.002158898,0.007601507,0.1634411,0.02982133,0.0451337,0.7480871],"study_design_scores_gemma":[0.00005566956,0.0002398813,0.01386404,0.0001810753,0.0001001774,0.002514226,0.002751587,0.08763789,0.2437164,0.01427177,0.6344255,0.0002418141],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03967125,0.0007260765,0.8794569,0.0004679407,0.0006263769,0.0003414101,0.002272608,0.01538103,0.06105639],"genre_scores_gemma":[0.2315811,0.001025955,0.7432967,0.00008198046,0.0002423584,0.0002386872,0.00340278,0.003186569,0.01694385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01555349,"threshold_uncertainty_score":0.05203164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01710077498530875,"score_gpt":0.2779481903567032,"score_spread":0.2608474153713944,"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."}}