{"id":"W6907269138","doi":"10.21233/nx8e-mc81","title":"Site 11, Georgian Bay, Canadian Hydrographic Service pollen surface sample dataset","year":2019,"lang":"en","type":"dataset","venue":"Neotoma Paleoecological Database","topic":"","field":"","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.002412623,0.002878941,0.002896513,0.001727699,0.001066905,0.0007080712,0.006294648,0.002790759,0.04659963],"category_scores_gemma":[0.002124326,0.002714027,0.0006002597,0.003763185,0.0006929599,0.001060374,0.002965697,0.003692305,0.1602581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001194965,"about_ca_system_score_gemma":0.002431133,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6122325,"about_ca_topic_score_gemma":0.9867958,"domain_scores_codex":[0.9844005,0.00183785,0.002029306,0.004757954,0.002154013,0.004820399],"domain_scores_gemma":[0.9811292,0.002758964,0.001213048,0.01004694,0.0004690526,0.004382776],"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":[0.000300087,0.0007191692,0.01118201,0.0006111806,0.0003029095,0.003536947,0.00001188801,0.0001381689,0.0000942265,0.00001283613,0.9830622,0.0000283623],"study_design_scores_gemma":[0.002260952,0.0004043671,0.01269969,0.0002218774,0.0008426771,0.0002369608,0.00004736638,0.0003238672,0.000008884973,0.00002052908,0.9797406,0.00319224],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0256576,0.0003716979,0.000002359935,0.0008372474,0.001388183,0.004420216,0.9666968,0.000487769,0.0001381417],"genre_scores_gemma":[0.0007800682,0.0003846172,0.001055758,0.009974183,0.0005963642,0.0003066299,0.9864451,0.0003606382,0.00009668597],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3745634,"threshold_uncertainty_score":0.9990818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03522344810136093,"score_gpt":0.2844228873843981,"score_spread":0.2491994392830372,"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."}}