{"id":"W6959391969","doi":"10.7910/dvn/kvc2wh","title":"Global Fisheries Methylmercury","year":2023,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Botanical Research and Chemistry","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Methylmercury; Fishing; Fisheries management; Global warming","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002247797,0.0002634685,0.0002933378,0.000004831394,0.0001574816,0.0001756324,0.00110853,0.000387765,0.03060241],"category_scores_gemma":[0.0007009458,0.0001025091,0.0001730862,0.0004283178,0.0001427688,0.0001280187,0.0007898106,0.0003382253,0.2227603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007163071,"about_ca_system_score_gemma":0.00003512363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003652075,"about_ca_topic_score_gemma":0.006816595,"domain_scores_codex":[0.9980804,0.00006978317,0.0002330504,0.0005276695,0.0005572473,0.0005318774],"domain_scores_gemma":[0.9990002,0.0002445292,0.00008624595,0.0002868989,0.00005816725,0.0003239789],"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":[0.00002995893,0.00004302802,0.00002057778,0.00003386015,0.00003592355,0.0001158439,3.232775e-7,4.758069e-8,0.0006031393,0.000002605273,0.9953065,0.003808217],"study_design_scores_gemma":[0.00006473935,0.00006028095,0.000801673,0.00003334124,0.00002846531,0.000006085781,0.00004970489,8.550492e-7,0.00004452644,0.0001124365,0.9985417,0.0002561879],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007390002,0.000002873431,1.086779e-7,0.00006867808,0.000273982,0.0001115463,0.9984185,0.0001141365,0.0002712252],"genre_scores_gemma":[0.00002492894,0.000782595,0.00001152912,0.000257346,0.0005798307,0.00002526809,0.9975568,0.000001082187,0.0007606286],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1921579,"threshold_uncertainty_score":0.9702837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02986997927997374,"score_gpt":0.2624174877435472,"score_spread":0.2325475084635734,"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."}}