{"id":"W6969572933","doi":"10.5683/sp3/6zgcdt","title":"Data and Code for: \"Food web structure across basins in Lake Erie, a large freshwater ecosystem\"","year":2024,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; University of Windsor","funders":"","keywords":"Code (set theory); Raw data; Food web; Drainage basin; Statistical analysis; Hydrology (agriculture)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":true,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001448053,0.00194381,0.001400925,0.002891752,0.001225066,0.001970567,0.003495279,0.001426966,0.1424678],"category_scores_gemma":[0.00593378,0.001127369,0.001432217,0.004927436,0.0005257224,0.001057542,0.002392631,0.00160081,0.0561744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002804767,"about_ca_system_score_gemma":0.004720887,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1412543,"about_ca_topic_score_gemma":0.2496266,"domain_scores_codex":[0.9990976,0.0001216219,0.000119123,0.000243891,0.0002227931,0.0001949416],"domain_scores_gemma":[0.9972839,0.0005989012,0.0003176805,0.0005381756,0.0008680319,0.000393332],"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":[0.00006048702,0.00001868902,0.00180289,0.0006216349,0.00005475784,0.00001644936,0.00004225595,0.0003901118,0.0001355687,0.0004862375,0.9950728,0.001298183],"study_design_scores_gemma":[0.0007671277,0.00002387599,0.02253412,0.0005202654,0.00009016618,0.00006209768,0.0001650319,0.0005863072,0.0005247141,0.00190518,0.9727392,0.00008205113],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001216647,0.00001176237,0.00007206444,0.00001999099,0.000007068072,0.00001343294,0.9993,0.0001765516,0.0002775057],"genre_scores_gemma":[0.0006103046,0.00001699472,0.0006014797,0.00004797547,0.000003396604,0.0002541703,0.997747,0.0001443972,0.0005742373],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8587457,"threshold_uncertainty_score":0.4766022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02943014679135073,"score_gpt":0.314573658450747,"score_spread":0.2851435116593962,"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."}}