{"id":"W2747545567","doi":"10.5194/isprs-archives-xlii-2-w5-719-2017","title":"PLANNING BY USING DIGITAL TECHNOLOGY IN THE RECONSTRUCTION OF CULTURAL HERITAGE SITES – A CASE STUDY OF QIONG-LIN SETTLEMENT IN KINMEN AREA","year":2017,"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":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cultural heritage; Intangible cultural heritage; Context (archaeology); Cultural heritage management; Human settlement; Digitization; Geography; World Wide Web; Archaeology; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007269307,0.0002602085,0.000169265,0.001022717,0.005371773,0.001468427,0.001206607,0.001020649,0.006111672],"category_scores_gemma":[0.00107941,0.0002661062,0.0002737697,0.001841982,0.002007568,0.001192246,0.002063531,0.0006762397,0.0003824122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003106389,"about_ca_system_score_gemma":0.003140306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05085038,"about_ca_topic_score_gemma":0.168719,"domain_scores_codex":[0.9994191,0.0002984007,0.0000216779,0.00004742084,0.00007945661,0.0001339998],"domain_scores_gemma":[0.9995698,0.000147203,0.00004635968,0.00004243939,0.00005833197,0.0001357919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001947958,0.0008086207,0.1004838,0.0008492471,0.00005280845,0.1366892,0.6247366,0.004536443,0.005922738,0.0162504,0.004085047,0.1053903],"study_design_scores_gemma":[0.00001477304,0.0002137678,0.0352593,0.0001945424,0.00003435313,0.006821477,0.8998003,0.002878611,0.001361529,0.001231687,0.05214433,0.00004529819],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824571,0.0002832255,0.001564169,0.0005332809,0.00001051457,0.0001010819,0.00004583237,0.00001370337,0.01499093],"genre_scores_gemma":[0.9932368,0.0002306114,0.002103689,0.00003932621,0.000002679067,0.00003371733,0.00002384127,0.000004422473,0.004324784],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05085038,"threshold_uncertainty_score":0.1011088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03454253497936892,"score_gpt":0.2768061718782639,"score_spread":0.242263636898895,"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."}}