{"id":"W6977127419","doi":"10.6073/pasta/fb4f5687339bec467ce0ed1ea0b5f0ca","title":"LAGOS-NE-GIS v1.0: A module for LAGOS-NE, a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of U.S. Lakes: 2013-1925","year":2017,"lang":"en","type":"dataset","venue":"Environmental Data Initiative","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geospatial analysis; Context (archaeology); Geographic information system; Water quality; Data quality; Spatial analysis; Wetland; Spatial database","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.002361167,0.001174779,0.002374455,0.0002496849,0.0005012181,0.0001111873,0.001836461,0.000809973,0.0007358862],"category_scores_gemma":[0.0009670548,0.000946892,0.0002593949,0.00005114757,0.002947375,0.001442211,0.003867934,0.0006314609,0.0001227677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001350663,"about_ca_system_score_gemma":0.0001090244,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001153338,"about_ca_topic_score_gemma":0.03314318,"domain_scores_codex":[0.9937349,0.0006761549,0.001745515,0.002171797,0.0007277649,0.0009438772],"domain_scores_gemma":[0.9928617,0.001258818,0.002149787,0.003284138,0.00006814127,0.00037739],"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":[0.004291325,0.003695972,0.007852873,0.001695708,0.001176999,0.00004072099,0.0006386774,0.000002663891,0.002837735,0.00000851837,0.9771349,0.0006239116],"study_design_scores_gemma":[0.02832834,0.003053648,0.04875397,0.0006420476,0.00213067,0.00003684024,0.002024613,0.001802043,0.004125367,0.0001379375,0.9063172,0.002647291],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.06014331,0.0003720088,0.0006051321,0.0000578624,0.0002049357,0.004511344,0.9340667,0.00003141967,0.000007323903],"genre_scores_gemma":[0.08336791,0.0006513376,0.004240554,0.0001521447,0.0001973327,0.0007604135,0.9104297,0.0001493744,0.00005127038],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07081766,"threshold_uncertainty_score":0.9997661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09998195672047969,"score_gpt":0.3511191167766012,"score_spread":0.2511371600561215,"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."}}