{"id":"W4239512968","doi":"10.5203/lwbin.ceos.2014.2","title":"Lake Winnipeg Watershed Basemap Layers","year":2014,"lang":"en","type":"dataset","venue":"UMANCEOS","topic":"Ecology and biodiversity studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Watershed; Environmental science; Hydrology (agriculture); Geography; Geology; Computer science; Geotechnical engineering","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001666667,0.0002313546,0.0002791035,0.00003263717,0.0003037139,0.0000158593,0.0004795205,0.000261417,0.01760649],"category_scores_gemma":[0.00002815635,0.0001928291,0.00008393657,0.00007099053,0.0003798274,0.00007078904,0.0005404664,0.0002751106,0.02298773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007528276,"about_ca_system_score_gemma":0.000004964098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002883457,"about_ca_topic_score_gemma":0.01366031,"domain_scores_codex":[0.9988341,0.00005219693,0.00014003,0.0004249925,0.0002097286,0.0003389782],"domain_scores_gemma":[0.9993894,0.00004202262,0.00009841067,0.0003985796,0.000004869946,0.00006675572],"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":[0.00001138469,0.0000348391,0.009309842,0.00002568601,0.00003120195,0.00002548822,0.00002905899,0.00001016077,0.000004718026,5.267639e-7,0.9904074,0.0001097151],"study_design_scores_gemma":[0.0002077798,0.00006727156,0.008272996,0.00001135336,0.00005515453,0.000002446897,0.00001771564,0.00000188806,0.00001844001,0.00002368039,0.991067,0.0002542939],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001333853,0.00002888142,0.000003748472,0.0006147922,0.000582853,0.0001942866,0.994702,0.00004170937,0.002497859],"genre_scores_gemma":[0.0005458004,0.0001536666,0.00007963515,0.002450556,0.0001667503,0.00001643968,0.9928474,0.000006758143,0.003733033],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01337197,"threshold_uncertainty_score":0.9832916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00951715714061235,"score_gpt":0.2013813219381442,"score_spread":0.1918641647975318,"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."}}