{"id":"W2586962796","doi":"10.5075/epfl-thesis-7328","title":"Potentialities of the urban volume : mapping underground resource potential and deciphering spatial economies and configurations of multi-level urban spaces","year":2016,"lang":"en","type":"article","venue":"Infoscience (Ecole Polytechnique Fédérale de Lausanne)","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Urbanization; Resource (disambiguation); Environmental planning; Urban planning; Civil engineering; Work (physics); Natural resource; Architectural engineering; Environmental resource management; Geography; Engineering; Computer science; Political science; Environmental science; Law","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002470253,0.0001848426,0.0002355355,0.0001354967,0.0002021446,0.00007210691,0.0003656581,0.0001285811,0.00001870207],"category_scores_gemma":[0.00009122171,0.0001521989,0.00005987362,0.0001906954,0.0006859061,0.0003100718,0.0001768227,0.0001200599,0.000001205867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000693818,"about_ca_system_score_gemma":0.00007768593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000681392,"about_ca_topic_score_gemma":0.0004585388,"domain_scores_codex":[0.9987764,0.00003924577,0.0004534395,0.0002511464,0.0001617181,0.0003180789],"domain_scores_gemma":[0.999159,0.0001053578,0.0001801243,0.0003855585,0.00007890724,0.00009109963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002029938,0.00008323367,0.03718808,0.0002735542,0.00008151516,0.000001667594,0.003956584,0.02395575,0.9032197,0.009777984,0.001178403,0.02026324],"study_design_scores_gemma":[0.001354283,0.0001871958,0.2490961,0.001086363,0.0001077294,0.00008074878,0.00183854,0.464131,0.2701634,0.003961563,0.006805197,0.001187927],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4202913,0.0001680929,0.5782194,0.0003801341,0.0000876499,0.0003389352,0.000102356,0.0001185933,0.0002935691],"genre_scores_gemma":[0.9778743,0.00007983779,0.02139664,0.00003177581,0.00005331786,0.00008388954,0.000001931789,0.00002325689,0.0004550121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6330563,"threshold_uncertainty_score":0.6206489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01643803401122273,"score_gpt":0.2079766412557864,"score_spread":0.1915386072445636,"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."}}