{"id":"W6907175648","doi":"10.21233/n3js7k","title":"Hayes Lake pollen dataset","year":2017,"lang":"en","type":"dataset","venue":"Neotoma Paleoecological Database","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Ontario Museum","funders":"","keywords":"Pollen; Vegetation (pathology); Raw data","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":["metaepi_narrow","sts","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.001808769,0.002086949,0.002424422,0.000676676,0.001464893,0.0009204761,0.008108041,0.001993315,0.06127108],"category_scores_gemma":[0.006674821,0.001717052,0.0005315935,0.0003692761,0.001651026,0.001037876,0.005317993,0.003281576,0.2205576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002807883,"about_ca_system_score_gemma":0.0006633293,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004087768,"about_ca_topic_score_gemma":0.05279206,"domain_scores_codex":[0.9897669,0.001009763,0.001512496,0.00338818,0.001873217,0.002449437],"domain_scores_gemma":[0.9835763,0.001174924,0.00187651,0.01176562,0.0002187254,0.001387954],"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":[0.0005766603,0.001260858,0.0002830682,0.0003021548,0.0002625246,0.0090473,0.000002274559,0.000001576819,0.00003219205,0.0000135664,0.9877996,0.0004181523],"study_design_scores_gemma":[0.002021644,0.0005044662,0.005729259,0.0002197941,0.0006820873,0.0002916429,0.000009724281,0.00001462264,0.00001123363,0.00005498992,0.9883627,0.002097792],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002541961,0.0004263581,0.00000286434,0.0002239919,0.001664589,0.002272449,0.9938733,0.0005777846,0.0007044843],"genre_scores_gemma":[0.00004691235,0.0005967629,0.0005973773,0.001549409,0.001629772,0.0008410263,0.9941083,0.0001897939,0.000440604],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1592865,"threshold_uncertainty_score":0.9998351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05211566526766111,"score_gpt":0.3579092766264014,"score_spread":0.3057936113587403,"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."}}