{"id":"W6964169007","doi":"10.25549/impa-c123-84302","title":"White waters of the Ubangi River, Congo, ca.1920-1940","year":2014,"lang":"en","type":"dataset","venue":"University of Southern California Digital Library","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Shore; White (mutation); Deep water; Nova scotia","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":[0.0004296819,0.00179603,0.0009888357,0.005503488,0.0009275582,0.002175383,0.001611878,0.0009289547,0.03270216],"category_scores_gemma":[0.002423698,0.0006362203,0.0006720774,0.01168301,0.0004313658,0.001205537,0.001433739,0.001138561,0.04335593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002186554,"about_ca_system_score_gemma":0.002933132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2727427,"about_ca_topic_score_gemma":0.4306728,"domain_scores_codex":[0.9995938,0.00004732299,0.00005295876,0.0001254544,0.00008454332,0.00009610687],"domain_scores_gemma":[0.9988232,0.0001438989,0.0002519165,0.0001839038,0.0004383528,0.0001585894],"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":[0.00004223729,0.00001241004,0.003042988,0.0004042444,0.00002430181,0.00003111446,0.00004956132,0.0001818495,0.00005125633,0.0002411581,0.9930495,0.002869398],"study_design_scores_gemma":[0.00007493662,0.00000863723,0.03902713,0.0004830699,0.00003070126,0.00007409416,0.0003368413,0.000318236,0.0002438814,0.0003012016,0.9590694,0.00003193906],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002712282,0.0001009536,0.00001353695,0.00004475447,0.00001806642,0.000002029664,0.9990079,0.0000881913,0.0004533802],"genre_scores_gemma":[0.001030859,0.000110549,0.00005429577,0.00001382755,0.000007738139,0.00001817527,0.9976271,0.00002834608,0.001109102],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2727427,"threshold_uncertainty_score":0.5423103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004967627346597519,"score_gpt":0.1485375583355409,"score_spread":0.1435699309889433,"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."}}