{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005784685,0.001940737,0.0009482368,0.00345918,0.000760547,0.001810601,0.002451247,0.001009012,0.04416838],"category_scores_gemma":[0.003911662,0.0009091526,0.0009502373,0.006428791,0.0003673997,0.0009065717,0.001346365,0.001235886,0.03355886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00225954,"about_ca_system_score_gemma":0.00493944,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3108815,"about_ca_topic_score_gemma":0.4655599,"domain_scores_codex":[0.999575,0.00005248565,0.00003216257,0.0001222317,0.000119145,0.00009895984],"domain_scores_gemma":[0.9989313,0.0001733433,0.00008667912,0.0002563312,0.0003963529,0.0001559507],"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":[0.00004059014,0.00001123214,0.0008599036,0.0001861466,0.00002565551,0.00001178174,0.00002666957,0.0006652451,0.00008098106,0.0003297428,0.9950143,0.002747703],"study_design_scores_gemma":[0.0002766221,0.000006391795,0.007893879,0.0001886914,0.00004524086,0.00002703146,0.00009875331,0.002369231,0.0005508351,0.001885846,0.9866222,0.00003517801],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003979061,0.00004463357,0.00013718,0.00004774917,0.00001638425,0.00001557164,0.9977708,0.0007173477,0.0008524635],"genre_scores_gemma":[0.0009026415,0.00005874597,0.000586233,0.00002146232,0.000003593274,0.00006908125,0.9969879,0.0002125559,0.001157676],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6891185,"threshold_uncertainty_score":0.6181439,"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."}}