{"id":"W2971515141","doi":"10.1079/9781789243307.0009","title":"Descriptive statistics: making sense of data.","year":2019,"lang":"en","type":"book-chapter","venue":"CABI eBooks","topic":"Forest ecology and management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Descriptive statistics; Variable (mathematics); Natural (archaeology); Statistics; Data science; Data mining; Geography; Mathematics; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001599669,0.0001829197,0.0002426822,0.00003632246,0.00004311596,0.00000779146,0.0003392816,0.0001587412,0.003655247],"category_scores_gemma":[0.00001144218,0.0001808057,0.00003425633,0.000004197436,0.0003946376,0.00004986194,0.0009973357,0.0001833124,0.001955939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001148385,"about_ca_system_score_gemma":0.00002453344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003657755,"about_ca_topic_score_gemma":0.0003246691,"domain_scores_codex":[0.9990101,0.00001358423,0.0002216666,0.0003970227,0.0001828948,0.0001747005],"domain_scores_gemma":[0.9989361,0.00004872862,0.000205327,0.0007676282,0.000007813884,0.00003446017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004114653,0.00001262371,0.0001812562,0.0001064419,0.0001339704,0.0001950244,0.0003977178,0.00005354334,0.00006720334,0.9074937,0.03496565,0.05635169],"study_design_scores_gemma":[0.0002381933,0.0001150437,0.0009768406,0.0001026906,0.0001614543,0.00001019355,0.0000347757,0.0002055438,0.00002455684,0.07897719,0.9187998,0.0003537404],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00008315469,0.00002120298,0.003571034,0.000007245036,0.0002813138,0.0003577797,0.0004152509,0.00001833005,0.9952447],"genre_scores_gemma":[0.00719403,0.000007828869,0.004203749,0.0001991247,0.00002986252,0.000003238393,0.0001042848,0.00003171175,0.9882262],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.8838341,"threshold_uncertainty_score":0.9988211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03824609604797616,"score_gpt":0.246026395361459,"score_spread":0.2077802993134828,"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."}}