Selenium and mercury in the Kootenai River, Montana and Idaho, 2018-2019
Bibliographic record
Abstract
Selenium loads have been increasing over time in the Elk River, British Columbia, Canada, due to coal mining operations and runoff from associate spoil piles. The Elk River is a tributary to the Kootenay/Kootenai River and Lake Koocanusa. Extensive fish tissue monitoring has been conducted in Lake Koocanusa to assess the potential impacts of selenium from the Elk River (http://deq.mt.gov/DEQAdmin/LakeKoocanusa) However, fewer data are available for the Kootenai River downstream of Lake Koocanusa (downstream of Libby Dam). This 2018-2019 study generated baseline data on selenium and mercury concentrations in fish tissue and selenium and nutrient data in the water column of the Kootenai River and principal tributaries in Montana and Idaho. This data release is organized in two parts or "child items" for the fish tissue and water data, respectively. In addition to this compilation, the data are also being released through the USGS National Water Information System (NWIS) http://waterdata.usgs.gov/nwis/.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".