Data and scripts used in water-quality trend analysis in the International Souris River Basin, Saskatchewan and Manitoba, Canada and North Dakota, United States 1970-2020
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A comprehensive study to evaluate water-quality trends in the International Souris River Basin, Saskatchewan and Manitoba, Canada and North Dakota, United States was completed by the U.S. Geological Survey (USGS) in cooperation with the International Joint Commission. In this dataset all files necessary to run trend models and produce results published in U.S. Geological Scientific Investigations Report 2023-5084 [Nustad, R.A., and Tatge, W.S., 2023, Comprehensive water-quality trend analysis for selected sites and constituents in the International Souris River Basin, Saskatchewan and Manitoba, Canada, and North Dakota, United States, 1970–2020: U.S. Geological Survey Scientific Investigations Report 2023–5084, 83 p., https://doi.org/ 10.3133/ sir20235084]. In addition, this dataset contains data for reservoir and Canadian streamflow, and water-quality by group (MI = major ion and dissolved solids, NUT = nutrients, PHY = physical parameters) contained in comma separated values (csv) files (site_flow and country/province_data) for selected sites used in trend and spatial analysis of the Souris River Basin. Streamflow data for the selected United States sites were gathered from the National Water Information System (https://nwis.waterdata.usgs.gov/nwis). Water-quality data for the selected sites in the United States were gathered from the National Water Quality Monitoring Council Water Quality Portal (https://www.waterqualitydata.us/) and collected by two additional agencies NDDEQ and the USGS. 
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it