{"id":"W6907243641","doi":"10.21233/n3mm2k","title":"Pink 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":"Natural Resources Canada","funders":"","keywords":"Pollen; Vegetation (pathology); Assemblage (archaeology); Table (database)","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":[0.001940045,0.002131413,0.002468525,0.0006811112,0.001440511,0.0008536829,0.008454865,0.002135241,0.05273413],"category_scores_gemma":[0.006206044,0.001770014,0.0005451332,0.0003998438,0.001621077,0.0009956912,0.00558116,0.003636868,0.2526061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000293925,"about_ca_system_score_gemma":0.0006600461,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003620101,"about_ca_topic_score_gemma":0.04006511,"domain_scores_codex":[0.9893985,0.001039894,0.001569931,0.003511284,0.001928431,0.002552001],"domain_scores_gemma":[0.9826692,0.001063464,0.001937181,0.01263918,0.0002244659,0.001466553],"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":[0.0005852526,0.001279698,0.0002715309,0.000302975,0.0002550741,0.00969914,0.000002101122,0.000001176546,0.00003753417,0.00001884382,0.9871575,0.0003892192],"study_design_scores_gemma":[0.002227027,0.000532485,0.00634397,0.0002153034,0.0006853653,0.0003240746,0.000007932181,0.00001428071,0.000008614137,0.00004950446,0.9874504,0.00214102],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002407648,0.0003660758,0.000003316634,0.0002276951,0.00181137,0.002447116,0.9935488,0.0005884214,0.0007665175],"genre_scores_gemma":[0.00003755994,0.000596023,0.0005526904,0.001871533,0.001789253,0.0008441226,0.9936869,0.0001979736,0.0004239446],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.199872,"threshold_uncertainty_score":0.9998595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0534204583264859,"score_gpt":0.3535709180005279,"score_spread":0.300150459674042,"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."}}