{"id":"W6925288228","doi":"10.1594/pangaea.982638","title":"Mixed layer depth reconstructions based on dinocyst assemblage data of sediment core HU-84-030-21PC","year":2025,"lang":"en","type":"dataset","venue":"Publishing Network for Geoscientific and Environmental Data (PANGAEA) (Alfred Wegener Institute for Polar and Marine Research)","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Dinocyst; Assemblage (archaeology); Mixed layer; Sediment core; Calibration; Core (optical fiber); Sediment","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009931329,0.001189281,0.0007692035,0.002661407,0.0003461762,0.00091099,0.001788578,0.001002338,0.01597013],"category_scores_gemma":[0.002207981,0.0005215236,0.0008184563,0.003264807,0.0002853919,0.0004835508,0.001086072,0.0008435682,0.01766882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001012819,"about_ca_system_score_gemma":0.001266377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02886153,"about_ca_topic_score_gemma":0.05505591,"domain_scores_codex":[0.999592,0.00005578719,0.00005188337,0.0001425117,0.00008473457,0.00007311991],"domain_scores_gemma":[0.9990045,0.0001472772,0.0001865564,0.0002278096,0.00033492,0.00009893526],"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":[0.0004999613,0.0001272647,0.03224193,0.001407859,0.0002568808,0.0002599515,0.0001335134,0.003757234,0.001494779,0.001843315,0.9404411,0.01753614],"study_design_scores_gemma":[0.0004374831,0.00005451238,0.09932698,0.0004102685,0.0001252852,0.0002270951,0.000210003,0.003263018,0.002050728,0.001581371,0.8922516,0.00006176609],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002593548,0.00007186981,0.0002283275,0.00003868074,0.00001873069,0.00001148003,0.9962335,0.0002099918,0.0005937278],"genre_scores_gemma":[0.002593238,0.00003410231,0.0006216779,0.00001980469,0.000006197173,0.00005142272,0.9958656,0.00003683627,0.0007710474],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02886153,"threshold_uncertainty_score":0.05738705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1611372315076294,"score_gpt":0.3416005777862336,"score_spread":0.1804633462786042,"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."}}