{"id":"W4394216092","doi":"10.6084/m9.figshare.16964656","title":"China-LDRL: China’s surface water bodies, Large Dams, Reservoirs, and Lakes dataset","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Environmental and Agricultural Sciences","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children","funders":"","keywords":"China; Surface water; Environmental science; Hydrology (agriculture); Geography; Water resource management; Geology; Environmental engineering; Archaeology; Geotechnical engineering","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000187951,0.0004760363,0.0003304343,0.000021931,0.000823328,0.0001848309,0.001311919,0.0001806185,0.9473557],"category_scores_gemma":[0.00006738208,0.00028146,0.0000745409,0.0001300268,0.00008073775,0.000550758,0.005034532,0.0005969259,0.007460375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001548234,"about_ca_system_score_gemma":0.000007057376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002099764,"about_ca_topic_score_gemma":0.002523136,"domain_scores_codex":[0.9970136,0.0001246948,0.0002811304,0.0009746402,0.0008548207,0.0007510863],"domain_scores_gemma":[0.9989592,0.00002822796,0.0001421216,0.0006151043,0.000001770124,0.0002535422],"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":[0.000004978739,0.00007988689,0.00005167182,0.00007137292,0.00001012135,0.00006527102,0.00006513484,0.00008876167,0.00009012168,8.873332e-8,0.9994075,0.0000650753],"study_design_scores_gemma":[0.0001435946,0.00007710401,0.01851589,0.0001022652,0.00001821192,0.0000291265,0.00005265412,0.000008181266,0.0000788796,0.00001282307,0.9804142,0.0005470446],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002041255,0.0003677223,3.999382e-9,0.0005714932,0.0001052559,0.0003648043,0.9962049,0.00003630215,0.0003082843],"genre_scores_gemma":[0.0002607593,0.0002197944,0.00001102353,0.0003254904,0.00007655028,0.00009374281,0.9969649,0.00001530552,0.002032463],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9398953,"threshold_uncertainty_score":0.9999638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01425031634625068,"score_gpt":0.226568488232582,"score_spread":0.2123181718863313,"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."}}