{"id":"W4394334709","doi":"10.6084/m9.figshare.19950896","title":"FIDMAC Standardized Yellow Perch Mercury Dataset for Canada","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Perch; Mercury (programming language); Fishery; Geography; Biology; Computer science; Fish <Actinopterygii>","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001998407,0.0003628,0.0005258584,0.0001598287,0.0004115869,0.0003418443,0.004161723,0.000134604,0.5049602],"category_scores_gemma":[0.001083317,0.0003636152,0.0001704166,0.0005105361,0.000006069442,0.0002521577,0.0018228,0.0004828706,0.000219409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002924146,"about_ca_system_score_gemma":0.00268674,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.09431012,"about_ca_topic_score_gemma":0.2301851,"domain_scores_codex":[0.9972409,0.0001165453,0.0003475285,0.0009417041,0.0008561855,0.0004971455],"domain_scores_gemma":[0.9966248,0.0004690186,0.0002537992,0.00234362,0.0001172612,0.0001914742],"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":[0.000004356126,0.0000151572,4.328561e-8,0.0001971908,0.0000739352,0.00009452076,0.000006769864,0.00002743347,1.986907e-7,6.441462e-7,0.9985367,0.001043066],"study_design_scores_gemma":[0.0002261152,0.00003783322,8.461011e-7,0.0001888643,0.00005909525,0.00001001122,0.00001590812,0.0008320673,0.000005034206,0.000003522185,0.9981649,0.0004558075],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[3.574464e-8,0.0002726124,0.00005709216,0.0002597156,0.0002935841,0.0002453632,0.9988148,0.00004039725,0.00001640418],"genre_scores_gemma":[2.548123e-7,0.0000171245,0.0006433901,0.00099562,0.0001711786,0.0005207894,0.9974123,0.00001982075,0.0002195101],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5047407,"threshold_uncertainty_score":0.9998816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03777790521936714,"score_gpt":0.279245386759033,"score_spread":0.2414674815396659,"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."}}