{"id":"W2973239477","doi":"10.37099/mtu.dc.etdr/913","title":"THE ARCHAEOLOGY OF THE POSTINDUSTRIAL: SPATIAL DATA INFRASTRUCTURES FOR STUDYING THE PAST IN THE PRESENT","year":2019,"lang":"en","type":"dissertation","venue":"","topic":"Archaeological Research and Protection","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Michigan Technological University; Social Sciences and Humanities Research Council of Canada; Michigan Space Grant Consortium; National Endowment for the Humanities; National Science Foundation","keywords":"Post-industrial society; Redevelopment; Big data; Archaeology; Geography; Data science; Engineering; Civil engineering; Computer science; Political science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002260254,0.000162429,0.0001892054,0.00004223168,0.0005249638,0.00004871537,0.00287945,0.0001903865,0.0003199836],"category_scores_gemma":[0.001619905,0.00004876621,0.00008187318,0.0002099218,0.0004288148,0.00006117782,0.000183777,0.0008668778,0.000006996856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003508958,"about_ca_system_score_gemma":0.0002004683,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02408236,"about_ca_topic_score_gemma":0.1725422,"domain_scores_codex":[0.9974402,0.0009450622,0.0003380233,0.0003045315,0.0005851372,0.0003870275],"domain_scores_gemma":[0.9937582,0.005048782,0.0002340925,0.0008544762,0.00007388464,0.00003054565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002232258,0.00001399386,0.1734412,0.00009292609,0.0001039942,0.000001708368,0.004582072,0.001521668,0.00001450176,0.0007364037,0.005674507,0.8115847],"study_design_scores_gemma":[0.0003215324,0.0004104223,0.9535524,0.00002506675,0.00002508429,0.000003929985,0.005216646,0.004197217,0.00004004367,0.01590284,0.02020484,0.0001000231],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9406785,0.002904247,0.001331348,0.02438461,0.004653763,0.01466577,0.001327639,0.00003083748,0.01002327],"genre_scores_gemma":[0.997107,0.0001787827,0.0000640873,0.0001096397,0.0004645168,0.0000318794,0.001054415,0.000004037275,0.000985612],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8114847,"threshold_uncertainty_score":0.9824163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07852085317418599,"score_gpt":0.3198547265944958,"score_spread":0.2413338734203098,"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."}}