Physicochemistry and metal concentrations in water, sediments, zooplankton, and Hyalella azteca from lakes in Yellowknife, NWT, Canada [2024]
Bibliographic record
Abstract
This dataset includes lake location information along with water physicochemical parameters (pH, anions, DOC, DIC, PARAFAC), major cations, and trace metals (ICP-MS/MS) in both dissolved (0.45 µm) and total water samples. Metal concentrations (ICP-MS/MS) are also provided for zooplankton, the amphipod Hyalella azteca, and sediments, along with carbon and sulfur stable isotope signatures. For H. azteca, both desorbed and non-desorbed (untreated) metal measurements are available for selected lakes. These data were collected in July 2024 to assess the potential of Hyalella azteca and zooplankton as biomonitors of rare earth elements (REEs), uranium (U), thorium (Th), and arsenic (As) across ten lakes in the Yellowknife region, NWT, Canada. For amphipods, the possible adsorption of metals on the exoskeleton was considered by completing desorption experiments, allowing us to investigate the potential mechanisms of REE bioaccumulation. We also examined REE bioaccumulation in amphipods across a depth gradient in relation to dietary shifts, using carbon and sulfur stable isotopes. A further objective was to establish baseline concentrations of REEs, U, and Th ahead of potential future mining activity in the region.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".