{"id":"W2162214842","doi":"","title":"U.S. and Canada Large Area Landmarks","year":2008,"lang":"en","type":"article","venue":"Offline data","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Feature (linguistics); Class (philosophy); Landmark; Generalization; Set (abstract data type); Geography; Cartography; Government (linguistics); Amusement; Computer science; Artificial intelligence; Mathematics; Psychology; Linguistics","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":[0.0003102261,0.0007009897,0.0005164125,0.003792659,0.002795008,0.002130904,0.001053674,0.0003351354,0.03665772],"category_scores_gemma":[0.002961646,0.0002705478,0.0003484868,0.01768715,0.0005413778,0.0008415362,0.000883024,0.0007033555,0.007840807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01491642,"about_ca_system_score_gemma":0.03443204,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9915763,"about_ca_topic_score_gemma":0.9956728,"domain_scores_codex":[0.9991611,0.00003173648,0.00002844064,0.00009418762,0.000511119,0.00017342],"domain_scores_gemma":[0.9951597,0.0001403285,0.0001513009,0.0001664742,0.004061432,0.0003208596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00008140427,0.00002371203,0.01717723,0.00009728924,0.00002292308,0.00007408501,0.0002537154,0.0006462983,0.0001259777,0.003788534,0.942941,0.0347678],"study_design_scores_gemma":[0.00003834802,0.00002057409,0.1112692,0.0001506042,0.00002733671,0.00009811458,0.001951336,0.00127755,0.0006281649,0.001018978,0.8834454,0.00007431803],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.02203807,0.0007242859,0.001331116,0.00159031,0.0001753339,0.0001428246,0.8094159,0.0007881388,0.1637941],"genre_scores_gemma":[0.1492641,0.002153176,0.006629348,0.0007695045,0.0000577092,0.0002339473,0.715712,0.0003649968,0.1248153],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03665772,"threshold_uncertainty_score":0.1226323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0621563957933131,"score_gpt":0.2838997383547791,"score_spread":0.221743342561466,"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."}}