{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001631797,0.001177906,0.0008541535,0.004248632,0.0004895341,0.002199106,0.001721552,0.0004282133,0.07608723],"category_scores_gemma":[0.004637512,0.001090626,0.000819449,0.007333594,0.0003270008,0.002997096,0.002422758,0.001028767,0.03987887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00135779,"about_ca_system_score_gemma":0.003083482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03910309,"about_ca_topic_score_gemma":0.04416225,"domain_scores_codex":[0.999245,0.0001094277,0.0001263669,0.000186139,0.0002572415,0.00007578589],"domain_scores_gemma":[0.9980662,0.000386281,0.0002429487,0.0003604097,0.0007353563,0.0002087322],"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":[0.0001642875,0.00003821225,0.007037185,0.0004206565,0.00007258412,0.00004642027,0.0001824117,0.00159805,0.0006240031,0.002852556,0.9670223,0.01994141],"study_design_scores_gemma":[0.0002132358,0.00002012455,0.01115524,0.0001700478,0.00003647959,0.00004718284,0.000242629,0.004137905,0.00147119,0.002860897,0.9795608,0.00008432156],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001307659,0.00005382712,0.00481144,0.00009793737,0.00003755148,0.0001410247,0.9776801,0.01160467,0.004265668],"genre_scores_gemma":[0.004111791,0.00009394049,0.01142336,0.00006280954,0.00001619668,0.0004534833,0.977991,0.003648107,0.002199367],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07608723,"threshold_uncertainty_score":0.2545372,"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."}}