Water-quality and streamflow data for United States and Canadian sites in the Red River Basin and scripts for trend analysis - Data supporting water-quality trend analysis in the Red River of the North basin, 1970-2017
Bibliographic record
Abstract
A comprehensive study to evaluate water-quality trends in the international Red River of the North basin and to assess water-quality conditions for Red River of the North crossing the international boundary near Emerson, Manitoba was completed by the U.S. Geological Survey (USGS) in cooperation with the International Joint Commission, North Dakota Department of Environmental Quality (NDDEQ) and Minnesota Pollution Control Agency (MPCA), and in collaboration with Manitoba Sustainable Development (MSD) and Environment and Climate Change Canada (ECCC). In this dataset a zipped folder is provided which contains all files necessary to run models and produce results published in U.S. Geological Scientific Investigations Report 2020-5079 [Nustad, R.A., and Vecchia, A.V., 2020, Water-quality trends for selected sites and constituents in the international Red River of the North Basin, Minnesota and North Dakota, United States, and Manitoba, Canada, 1970-2017: U.S. Geological Survey Scientific Investigations Report 2020-5079, 75 p., https://doi.org/10.3133/sir20205079]. In addition, this dataset contains data for streamflow, and water-quality by site contained in three core types of comma separated values (csv) files (site_flow, site_qw_ions, and site_qw_nuts) for 34 sites used in trend analysis of the Red River of the North Basin. Streamflow data for the 24 U.S. sites were gathered from the National Water Information System (https://nwis.waterdata.usgs.gov/nwis). Streamflow data for the 10 Canadian sites were provided to the USGS in excel spreadsheets through email communication and were collected by Water Survey of Canada. Water-quality data for the 24 U.S. sites were gathered from the National Water Quality Monitoring Council Water Quality Portal (https://www.waterqualitydata.us/) and collected by four U.S. government agencies: MPCA; Minnesota Department of Agriculture (MNDA), NDDEQ (NDHD, formerly North Dakota Department of Health); and USGS. Water-quality data for the 10 Canadian sites were provided to the USGS in excel spreadsheets through email communication and were collected by two Canadian government agencies: ECCC and MSD. Data for sites, RREmerson_2 and PemRWindygates_38, were provided by ECCC. Data for sites, BoRCarman_32, CCrSpringfield_36, LaSRLaBarrierePark_33, RoRDominionCity_30, RatROtterburne_31, RRfloodway_1, RRSelkirk_3, SRSouthPerimeterHwy_34, were provided by MSD. Each child page contains 34 csv files containing data for each site.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".