{"id":"W3094289309","doi":"10.22541/au.160337371.17978204/v1","title":"Kejimkujik Calibrated Catchments: a benchmark dataset for long-term impacts of terrestrial acidification","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Soil and Water Nutrient Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of Nova Scotia; Acadia University; Dalhousie University","funders":"","keywords":"Environmental science; STREAMS; Wetland; Context (archaeology); Terrestrial ecosystem; Ecosystem; Climate change; Drainage basin; Water quality; Hydrology (agriculture); Precipitation; Geography; Ecology; Archaeology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000551876,0.0004108043,0.0003795605,0.001202484,0.0005404866,0.0007714619,0.0007192426,0.000631078,0.00129264],"category_scores_gemma":[0.001964434,0.0002463225,0.0003454164,0.002640749,0.0003038505,0.0003801725,0.00074454,0.0004474641,0.0005731393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001298814,"about_ca_system_score_gemma":0.002300075,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2338726,"about_ca_topic_score_gemma":0.244934,"domain_scores_codex":[0.9997053,0.00002850692,0.00002336968,0.0001030863,0.0000667085,0.00007304832],"domain_scores_gemma":[0.9988248,0.0001008354,0.0001502259,0.0003326857,0.0004135272,0.0001779334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001153284,0.0004599896,0.7512227,0.0003323802,0.0005440126,0.0006710883,0.0003632502,0.05905313,0.004662023,0.002548548,0.1456904,0.03329932],"study_design_scores_gemma":[0.000213246,0.00003932965,0.9165846,0.00004309692,0.0000800109,0.0001214379,0.0002881954,0.04944148,0.000953714,0.0006230384,0.03156716,0.00004464537],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.581735,0.0001933117,0.001400367,0.0001692794,0.00004408734,0.000128231,0.4127202,0.0004401874,0.003169427],"genre_scores_gemma":[0.3608172,0.00009600719,0.003333865,0.00004766954,0.00001975025,0.0001552201,0.6345423,0.00008290738,0.0009051167],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.7661274,"threshold_uncertainty_score":0.4650226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03294420128260016,"score_gpt":0.2884953065570025,"score_spread":0.2555511052744023,"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."}}