{"id":"W7133382163","doi":"","title":"Introduction","year":2022,"lang":"en","type":"other","venue":"RUNE (Research UNE)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Human settlement; Population; Boom; Settlement (finance); State (computer science); Rural area; Government (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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.003004461,0.0003347982,0.0004069209,0.002453943,0.0003179263,0.00009393072,0.0009825671,0.0002750134,0.4848395],"category_scores_gemma":[0.0006277288,0.0003535421,0.0001305687,0.003249729,0.0004342486,0.00007408008,0.0008335772,0.002492364,0.1403078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001027437,"about_ca_system_score_gemma":0.0004009508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005724993,"about_ca_topic_score_gemma":0.001266572,"domain_scores_codex":[0.993381,0.00119752,0.0002425974,0.001049189,0.002975035,0.001154608],"domain_scores_gemma":[0.9977105,0.0001241565,0.0001317637,0.001640451,0.0001640613,0.0002290456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004169504,0.0001573859,0.00003067833,0.00008003888,0.0001284006,0.00006573947,0.00008308216,0.000005416374,0.001008905,0.005013306,0.9914625,0.001922901],"study_design_scores_gemma":[0.0003550645,0.000115674,0.00007006886,0.00002493915,0.00001520242,0.00001485313,0.0001819081,0.00001462091,0.00006173551,0.0004924475,0.9983301,0.0003233816],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00002084268,0.002145179,0.000005283979,0.00501773,0.0009911609,0.001030916,0.0003284912,0.001047149,0.9894133],"genre_scores_gemma":[0.0001637553,0.0002970779,0.0001885075,0.00002658069,0.008524137,0.0003338882,0.0009872279,0.003348504,0.9861303],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3445316,"threshold_uncertainty_score":0.9998916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07592908229059539,"score_gpt":0.3999527002716143,"score_spread":0.3240236179810189,"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."}}