{"id":"W4396535651","doi":"10.24124/2018/59483","title":"Archaeological risk framework tool: application of predictive modelling in archaeology (a case study of Prince George municipal)","year":2018,"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":"","keywords":"George (robot); Statistic; Archaeology; Terrain; Logistic regression; Predictability; Elevation (ballistics); Sample (material); Predictive modelling; Predictive power; Computer science; History; Geography; Cartography; Engineering; Statistics; Artificial intelligence; Mathematics; Machine learning","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":[0.003091436,0.0006485321,0.0003310939,0.00243851,0.0009595256,0.001983749,0.001459181,0.0008649629,0.005254433],"category_scores_gemma":[0.008123187,0.0003504296,0.0006882744,0.002176611,0.000702707,0.001368126,0.001879771,0.0009117155,0.00055365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002051485,"about_ca_system_score_gemma":0.002011768,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04248313,"about_ca_topic_score_gemma":0.03996668,"domain_scores_codex":[0.9990643,0.0005247988,0.00004616232,0.0001074246,0.0001972025,0.0000601206],"domain_scores_gemma":[0.9967424,0.002382628,0.0002399096,0.0002248414,0.0003361677,0.00007408381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002600992,0.0003664735,0.08365643,0.0005534182,0.0001928814,0.003111373,0.01141524,0.4489229,0.001306,0.105922,0.01110202,0.3331911],"study_design_scores_gemma":[0.00003171762,0.0001389399,0.02068442,0.0003452252,0.00009041958,0.0009955266,0.007802919,0.8986427,0.001607,0.03965974,0.02991485,0.00008660509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4379441,0.000443462,0.5191804,0.003326539,0.00005577629,0.0006267119,0.003168977,0.001817587,0.03343656],"genre_scores_gemma":[0.763803,0.0003127917,0.2304253,0.00005896855,0.00001379299,0.0002608091,0.0009193469,0.0001171216,0.004088911],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9575168,"threshold_uncertainty_score":0.0844717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03059508038369157,"score_gpt":0.3015157664103856,"score_spread":0.270920686026694,"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."}}