{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007200433,0.001080477,0.0009140839,0.005481177,0.001658385,0.002043822,0.002669665,0.0008224494,0.07763232],"category_scores_gemma":[0.005255467,0.0005744444,0.001092161,0.01130916,0.0003336768,0.001050608,0.001401038,0.001249409,0.04457976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01090718,"about_ca_system_score_gemma":0.02852591,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9369453,"about_ca_topic_score_gemma":0.9483277,"domain_scores_codex":[0.9989609,0.00004164748,0.0001120705,0.000210004,0.0004773897,0.0001980458],"domain_scores_gemma":[0.9945615,0.0002618981,0.0002605041,0.0003688913,0.004271511,0.0002757388],"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":[0.0000400544,0.000007690547,0.002535187,0.0001928615,0.00002430544,0.00001558858,0.00002323148,0.0001970945,0.00007149839,0.0004000457,0.9921469,0.004345614],"study_design_scores_gemma":[0.00009023089,0.000007412333,0.02194788,0.0002805458,0.00004024322,0.00003879176,0.0001554332,0.0007110685,0.0004554778,0.000756801,0.9754557,0.00006034963],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001448219,0.00002245414,0.0000914218,0.00004530799,0.000009804354,0.00001723977,0.9983348,0.0002459106,0.00108816],"genre_scores_gemma":[0.0009066344,0.00006473352,0.0007677146,0.00006048779,0.000005015663,0.000113717,0.9958724,0.0001489019,0.002060359],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07763232,"threshold_uncertainty_score":0.259706,"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."}}