{"id":"W6926310424","doi":"10.21233/aj7a-zd39","title":"Site 22, Georgian Bay, Canadian Hydrographic Service pollen surface sample dataset","year":2019,"lang":"en","type":"dataset","venue":"Neotoma Paleoecological Database","topic":"History of Computing Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Paleoecology; Hydrography; Pollen; Sample (material); Georgian; Service (business)","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":[0.0006882141,0.001277591,0.000964855,0.004800023,0.001881412,0.001907201,0.00259356,0.0009835402,0.02814396],"category_scores_gemma":[0.005095852,0.0005256642,0.0007047951,0.01325179,0.0005793609,0.0008743515,0.001541956,0.001518855,0.03486886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007570001,"about_ca_system_score_gemma":0.01928319,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8323426,"about_ca_topic_score_gemma":0.9301031,"domain_scores_codex":[0.9990237,0.00004970248,0.00007947852,0.0002304095,0.00042377,0.000193057],"domain_scores_gemma":[0.9963627,0.000211039,0.0001667413,0.0004479808,0.002398042,0.0004134442],"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":[0.0000263221,0.000009327221,0.001502931,0.0001601027,0.00001609087,0.00001559994,0.00002449244,0.0001375827,0.00008681105,0.0002804484,0.9962586,0.001481671],"study_design_scores_gemma":[0.0001199347,0.000007246624,0.03049228,0.0002275857,0.00002982429,0.00003976312,0.0002443536,0.0004413336,0.0003638061,0.0006077129,0.9673823,0.00004398904],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000194852,0.00002857307,0.00002570305,0.00002992088,0.000009949264,0.000009194758,0.9989555,0.0001015947,0.0006446355],"genre_scores_gemma":[0.0004078276,0.00002967912,0.000145932,0.00001962231,0.000002482212,0.00002774758,0.9988412,0.0000314954,0.0004939199],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1676574,"threshold_uncertainty_score":0.3372896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02623565170144206,"score_gpt":0.2573196254817599,"score_spread":0.2310839737803178,"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."}}